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resume_builder/experience/experience_bosch.md

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+# Experience: (Senior) Data Engineer / Data Analysis Engineer — Robert Bosch Semiconductor Manufacturing Dresden GmbH
+## February 2020 – December 2022 | Dresden, Germany
+
+### Cross-Position Section
+
+**Promotion within Bosch (confirmed by LinkedIn):**
+| Level | Period | Duration |
+|-------|--------|----------|
+| Data Engineer / Data Analysis | Feb 2020 – Jan 2021 | 1 year |
+| **Senior** Data Engineer / Data Analysis | Jan 2021 – Dec 2022 | 2 years |
+
+**Title flexibility (IMPORTANT for generation):** The role was heavily data engineering in nature. Adapt the displayed title to the JD:
+- JD targets Data Engineer / Senior Data Engineer → use "(Senior) Data Engineer"
+- JD targets analytics-adjacent roles → use "(Senior) Data Analysis Engineer"
+- JD targets ML Engineer → use "Data & ML Engineer" (safe given BS-1 ML deployment work)
+- Show promotion when space allows: "Data Engineer → Senior Data Engineer" or "(Senior) Data Engineer"
+- Always show the promotion arc if the JD values seniority signals
+
+**Career arc framing:** Bosch was Dennis's most technical pre-Swisscom role — applying data engineering and ML at scale in a 24/7 semiconductor manufacturing environment. He operated as Application Owner (not just developer), introduced containerized ML inference into production lines, and built monitoring infrastructure from scratch. Promoted from mid-level to Senior after one year. The Zeugnis rates performance as "sehr gut" (top tier) — employer deeply regrets departure.
+
+**Semiconductor data domains (for targeting semi industry JDs):**
+Dennis worked across multiple data domains within the fab — these are domain signals for semiconductor company applications:
+- **Defect Management** — tracking, classifying, and analyzing wafer/chip defects; image-based defect detection
+- **Semiconductor Parameter Testing** — electrical parametric test data analysis across production lots
+- **Process Analysis** — correlating process parameters to yield and quality outcomes
+
+This domain expertise is rare in data engineering candidates and is a strong differentiator for semiconductor company JDs (ASML, Infineon, TSMC, GlobalFoundries, etc.). Flag this domain when targeting semi roles.
+
+**CL framing:** "At Bosch Semiconductor in Dresden, I worked at the intersection of data engineering and semiconductor manufacturing analytics — owning the data pipelines and applications that drove defect management, parameter testing analysis, and process optimization across 300mm wafer production. My most impactful project was containerizing ML inference for automated image-based defect classification, turning a manual quality inspection process into a fully automated, 24/7 production system."
+
+---
+
+### Achievement BS-1: ML Inference Containerization in 24/7 Production Environment
+
+**Source:** thiessen_cv_master_profile.md, thiessen_zeugnis_bosch.md
+**User's role:** Primary developer / strategy owner ("Erarbeitung und Durchführung" = designed AND executed)
+**Status:** Deployed to production
+
+**Context:** 300mm semiconductor fab runs continuously. Manual image classification for wafer defect detection created a bottleneck for line engineers in the Defect Management domain. Dennis designed and implemented the strategy to containerize and orchestrate ML inference into the live production pipeline — enabling fully automated defect classification with no manual intervention.
+
+**Bullet variants:**
+- **2L:** Containerized and orchestrated ML inference (Docker, Kubernetes, Ansible) into 24/7 semiconductor production pipelines, enabling fully automated image-based defect classification and significantly reducing manual inspection workload for line engineers.
+- **3L:** Designed and executed the ML inference integration strategy for Bosch's 24/7 semiconductor fab — containerizing defect-detection models with Docker, orchestrating via Kubernetes and Ansible, and embedding automated image classification into the Defect Management pipeline; eliminated manual wafer inspection bottleneck and enabled continuous, unattended quality monitoring across active 300mm production lines.
+- **1L:** Containerized ML inference (Docker, K8s, Ansible) for automated image-based defect classification in 24/7 semiconductor fab.
+
+**Key skills:** Docker, Kubernetes, Ansible, ML deployment, ML inference, containerization, MLOps, image classification, defect detection, production ML, semiconductor manufacturing
+**ATS keywords:** ML deployment, Kubernetes, Docker, Ansible, MLOps, inference, containerization, production ML, defect management, image classification, semiconductor
+**Reframing notes:**
+- ML/AI: this is the flagship bullet — always leads for ML/AI role type
+- Data Platform/Infra: emphasize K8s/Docker/Ansible infrastructure angle
+- Staff/Senior DE: include as evidence of ML pipeline ownership; frame around end-to-end delivery
+- Analytics Engineer: LOW — omit or condense
+
+---
+
+### Achievement BS-2: Data Services Over OracleDB and Hadoop/ImpalaSQL
+
+**Source:** thiessen_cv_master_profile.md, thiessen_zeugnis_bosch.md
+**User's role:** Primary developer
+**Status:** Deployed / operational
+
+**Context:** Built Python, Java, and C# data services consuming from OracleDB and Hadoop/ImpalaSQL to supply internal analysis teams with structured data and insights for semiconductor process optimization.
+
+**Bullet variants:**
+- **2L:** Built data services in Python, Java and C# over OracleDB and Hadoop/ImpalaSQL, supplying semiconductor analysis teams with on-demand data access and insights for process optimization and quality monitoring.
+- **3L:** Developed multi-language data services (Python, Java, C#) on top of OracleDB and Hadoop/ImpalaSQL, providing analysis teams with reliable, structured access to manufacturing process data; optimized query performance and data availability for downstream analytics in a high-throughput 24/7 fab environment.
+- **1L:** Built Python/Java/C# data services over OracleDB and Hadoop/ImpalaSQL for semiconductor analysis teams.
+
+**Key skills:** Python, Java, C#, OracleDB, Hadoop, ImpalaSQL, data services, query optimization
+**ATS keywords:** Python, Java, OracleDB, Hadoop, Impala, data pipeline, data services
+**Reframing notes:**
+- Staff/Senior DE: supporting bullet — shows multi-language data service depth
+- Data Platform/Infra: include for OracleDB + Hadoop coverage
+- ML/AI: secondary — omit or condense; ML data feed angle if needed
+- Analytics Engineer: LOW — omit
+
+---
+
+### Achievement BS-3: Application Owner — Analytics Platforms
+
+**Source:** thiessen_zeugnis_bosch.md, thiessen_cv_master_profile.md
+**User's role:** Application Owner — confirmed by Zeugnis as primary title/role
+**Status:** Ongoing ownership during employment
+
+**Context:** Beyond development, Dennis held explicit Application Owner responsibility for the semiconductor data analysis software suite — managing SLOs, vendor communication, internal customer relationships, training, and documentation.
+
+**Bullet variants:**
+- **2L:** Served as Application Owner for semiconductor data analysis applications and upstream pipelines, defining SLOs, delivering user training and documentation, and managing vendor relationships to ensure reliable 24/7 system operations.
+- **3L:** Held Application Owner responsibility for the semiconductor analytics software suite and upstream data pipelines — defined SLOs, delivered training and technical documentation, managed communication with software vendors and internal stakeholders across Fertigungstechnologie teams; ensured stable 24/7 operations while coordinating adoption across analysis teams in a fast-moving production environment.
+- **1L:** Application Owner for semiconductor analytics software suite — SLOs, vendor management, user training, documentation.
+
+**Key skills:** Application ownership, stakeholder management, SLO definition, vendor management, technical training, documentation
+**ATS keywords:** application owner, SLO, stakeholder management, technical documentation, vendor management
+**Reframing notes:**
+- Staff/Senior DE: strong ownership signal — include to demonstrate senior-level accountability
+- All role types: valuable as a "breadth" bullet showing beyond pure development; usually included
+- Analytics Engineer: frame around "enabling reliable data access for analysis teams"
+
+---
+
+### Achievement BS-4: ELK Stack PoC — Anomaly Detection & Monitoring
+
+**Source:** thiessen_cv_master_profile.md (CV-2), thiessen_zeugnis_bosch.md
+**User's role:** Primary developer
+**Status:** Proof of concept — confirmed in CV-2 and Zeugnis
+
+**Context:** Implemented a proof of concept using Elastic Stack (Elasticsearch, Logstash, Kibana) with Kafka for log ingestion and anomaly detection. Added Grafana, Prometheus, and Loki for monitoring and alerting across semiconductor manufacturing systems.
+
+**Bullet variants:**
+- **2L:** Delivered anomaly detection PoC using ELK Stack and Kafka (Docker) with Grafana, Prometheus and Loki monitoring — demonstrating centralized log management and alerting capability for 24/7 semiconductor manufacturing infrastructure.
+- **3L:** Designed and implemented an anomaly detection proof of concept using Elasticsearch, Logstash and Kibana (ELK) with Apache Kafka for log ingestion, containerized via Docker; added full observability stack with Grafana dashboards, Prometheus metrics and Loki log aggregation — validating centralized monitoring and alerting for high-volume 24/7 semiconductor production systems.
+- **1L:** ELK + Kafka PoC for anomaly detection; Grafana/Prometheus/Loki monitoring across semiconductor production.
+
+**Key skills:** ELK Stack, Elasticsearch, Logstash, Kibana, Kafka, Grafana, Prometheus, Loki, Docker, observability, anomaly detection
+**ATS keywords:** ELK Stack, Elasticsearch, Kafka, Grafana, Prometheus, observability, monitoring, anomaly detection
+**Reframing notes:**
+- Data Platform/Infra: HIGH — leads with observability and monitoring stack
+- Staff/Senior DE: include as supporting signal for platform depth; PoC framing acceptable
+- ML/AI: frame anomaly detection angle for MLOps/monitoring fit
+- Analytics Engineer: LOW — omit
+
+**Note:** This bullet appears in CV-2 only. Include when resume has budget; omit on tight 1-page version.
+
+---
+
+### Achievement BS-5: Tibco Spotfire C# Extensions
+
+**Source:** thiessen_linkedin_profile.md
+**User's role:** Developer
+**Status:** Deployed
+
+**Context:** Developed C# extensions for Tibco Spotfire data analysis tool, extending its visualization and analysis capabilities within the semiconductor manufacturing analytics environment.
+
+**Bullet variants:**
+- **1L:** Developed C# extensions for Tibco Spotfire to extend analytics capabilities within the semiconductor data environment.
+
+**Key skills:** C#, Tibco Spotfire, BI tooling, data visualization
+**ATS keywords:** Tibco Spotfire, C#, BI, data visualization
+**Reframing notes:**
+- Analytics Engineer: niche signal — include if JD mentions Spotfire or BI tooling
+- All other: LOW — omit; roll into skills section if relevant
+
+---
+
+## Position Summary
+
+| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
+|-------------|----|-----------------|-----------------------|--------------------|----------------------|
+| ML Inference Containerization | BS-1 | HIGH | LOW | HIGH | HIGH |
+| Data Services (Oracle/Hadoop) | BS-2 | HIGH | MED | MED | HIGH |
+| Application Owner | BS-3 | HIGH | HIGH | MED | HIGH |
+| ELK PoC / Monitoring | BS-4 | MED | LOW | MED | HIGH |
+| Tibco Spotfire Extensions | BS-5 | LOW | MED | LOW | LOW |

resume_builder/experience/experience_capgemini.md

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+# Experience: Software Engineer — Capgemini Deutschland GmbH
+## November 2014 – May 2015 | Hamburg, Germany
+
+### Cross-Position Section
+
+**Career arc framing:** Capgemini was Dennis's first professional role after the Bundeswehr — a 6-month consulting engagement in the Technology Services APPS division, working on test automation for a transport logistics client. Despite its brevity, the Zeugnis rates performance as "sehr gut" (vollsten Zufriedenheit — top tier), and the employer deeply regrets departure. This role anchors the test automation thread that runs through Generali and Vizrt. Typically omitted from 2-page resumes; included in full CV for timeline completeness.
+
+**Resume recommendation:** Omit from 2-page resume unless targeting roles where test automation breadth from earliest career adds value. Include on full CV (5-page) under early career section.
+
+---
+
+### Achievement CA-1: GUI Test Automation for Transport Logistics Client
+
+**Source:** thiessen_zeugnis_capgemini.md
+**User's role:** Developer / implementer
+**Status:** Deployed
+
+**Context:** Working on a Capgemini client project for a leading transport logistics company. Dennis planned and implemented test automation using Capgemini's internal GUI test framework and HP Quality Center (ALM), adapted existing automated test cases, implemented new ones from design specs, monitored automated runs, and coordinated bug fixes.
+
+**Bullet variants:**
+- **2L:** Implemented and managed GUI test automation suite for a transport logistics client using HP Quality Center (ALM) — planned, built and maintained automated test cases from design specifications, monitored test runs, and coordinated bug-fix cycles.
+- **1L:** Implemented GUI test automation (HP Quality Center) for transport logistics client; managed full test cycle from planning to bug-fix coordination.
+
+**Key skills:** Test automation, GUI testing, HP Quality Center (ALM), test planning, defect management
+**ATS keywords:** test automation, HP Quality Center, ALM, GUI testing, defect management, quality assurance
+**Reframing notes:**
+- All roles: LOW on resume — too early-career and non-core for target roles
+- Full CV: include for timeline completeness and to show consistent test automation background from day one
+- Consulting context: frame as "client-facing" to show early consulting exposure
+
+---
+
+## Position Summary
+
+| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
+|-------------|----|-----------------|-----------------------|--------------------|----------------------|
+| GUI Test Automation | CA-1 | LOW | LOW | LOW | LOW |
+
+**Resume inclusion guidance:**
+- 2-page resume: **Omit entirely**
+- 5-page CV: Include as single condensed bullet under "Early Career" or within timeline continuity section
+- Cover letter: Not relevant to mention

resume_builder/experience/experience_fraunhofer.md

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+# Experience: Research Software Engineer — Fraunhofer-Center für Maritime Logistik und Dienstleistungen CML
+## September 2018 – October 2019 | Hamburg, Germany
+
+### Cross-Position Section
+
+**Career arc framing:** Fraunhofer CML bridges the test-automation / consulting era (Generali, Vizrt) with the data engineering / ML focus that defines Bosch and Swisscom. Here Dennis transitioned from pure software development into applied ML research — working on NLP for sea rescue transcription (ARTUS) and microservice architectures for maritime data exchange (MISSION). He also introduced CI/CD to the team independently. Fixed-term research contract, left by mutual agreement.
+
+**CL framing:** "At Fraunhofer CML's maritime research center, I moved beyond product software into applied research — developing ML and NLP components for an automatic transcription system for sea rescue (ARTUS), and building the microservice backbone for a maritime data exchange platform (MISSION). I also brought CI/CD discipline to the team by independently setting up Jenkins-based build automation with quality gates."
+
+---
+
+### Achievement FC-1: SCEDAS Crew Scheduling System Development & CI/CD
+
+**Source:** thiessen_cv_master_profile.md, thiessen_zeugnis_fraunhofer.md
+**User's role:** Developer (C# app dev + CI/CD sole setup)
+**Status:** Deployed / operational software
+
+**Context:** SCEDAS® is a Decision Support System for maritime crew scheduling with mathematical heuristics for optimal planning. Dennis developed features, fixed bugs, and independently set up the Jenkins CI/CD pipeline with quality gates — the first build automation at the team.
+
+**Bullet variants:**
+- **2L:** Developed and maintained SCEDAS crew scheduling software (C#, .NET, MS SQL, Entity Framework); independently established Jenkins CI/CD pipeline with quality gates, introducing build automation and continuous deployment to the team.
+- **3L:** Extended and maintained SCEDAS®, Fraunhofer CML's crew scheduling Decision Support System (C#, .NET, MS SQL Server, Entity Framework), improving runtime performance and correctness through increased test coverage; independently introduced Jenkins-based CI/CD pipeline with build automation and quality gates — the first deployment automation adopted by the team.
+- **1L:** Developed SCEDAS (C#/.NET/SQL) crew scheduling DSS; independently set up Jenkins CI/CD pipeline with quality gates.
+
+**Key skills:** C#, .NET, Entity Framework, MS SQL Server, Jenkins, CI/CD, build automation, quality gates, software testing
+**ATS keywords:** C#, .NET, SQL Server, Jenkins, CI/CD, build automation, continuous deployment
+**Reframing notes:**
+- Staff/Senior DE: frame CI/CD introduction as initiative signal — independent, not assigned
+- ML/AI: LOW — omit SCEDAS; mention only if CI/CD context is relevant
+- All roles: CI/CD setup is the key signal here; SCEDAS itself is context
+
+---
+
+### Achievement FC-2: ARTUS — ML/NLP for Sea Rescue Transcription
+
+**Source:** thiessen_zeugnis_fraunhofer.md, thiessen_cv_master_profile.md
+**User's role:** Contributing developer (research team effort — hedge verbs)
+**Status:** Research project
+
+**Context:** ARTUS was a Fraunhofer CML research project to develop an automatic transcription system for sea rescue operations using speech recognition and ML. Dennis contributed ML and NLP development components within the research team.
+
+**Bullet variants:**
+- **2L:** Contributed ML and NLP components to ARTUS, a Fraunhofer research project developing an automatic speech transcription system for sea rescue operations — applying speech recognition and machine learning to a safety-critical maritime domain.
+- **3L:** Developed ML and speech recognition components for ARTUS, a Fraunhofer CML research initiative targeting automatic transcription of sea rescue communications; contributed to the NLP pipeline and model development, bringing machine learning capabilities to a safety-critical domain with no prior automated transcription tooling at the center.
+- **1L:** Contributed ML/NLP components to ARTUS — sea rescue speech transcription system (Fraunhofer research).
+
+**Key skills:** ML, NLP, speech recognition, Python, research, maritime safety
+**ATS keywords:** NLP, machine learning, speech recognition, Python, research
+**Reframing notes:**
+- ML/AI: HIGH — lead as evidence of applied ML/NLP research experience
+- Staff/Senior DE: MED — include as ML breadth signal; hedge verb "Contributed"
+- Analytics/Platform: LOW — omit
+
+**Provenance:** "Entwicklungstätigkeiten" in Zeugnis = development work within a research team. Use "Contributed" not "Led". Do not claim sole ownership of ARTUS.
+
+---
+
+### Achievement FC-3: MISSION — Maritime Microservice Data Exchange Platform
+
+**Source:** thiessen_zeugnis_fraunhofer.md, thiessen_cv_master_profile.md
+**User's role:** Developer (microservices for the platform)
+**Status:** Research prototype
+
+**Context:** MISSION was a Fraunhofer CML research project to build a maritime data exchange platform. Dennis built microservices using Express.js, JavaScript, Docker, and SQLite to enable data exchange between maritime stakeholders.
+
+**Bullet variants:**
+- **2L:** Built microservices (Express.js, JavaScript, Docker, SQLite) for MISSION, a Fraunhofer research project creating a maritime data exchange platform — enabling structured data interchange between maritime logistics stakeholders.
+- **3L:** Developed the microservice layer for MISSION, a Fraunhofer CML research platform for maritime data exchange — built REST services in Express.js and JavaScript, containerized with Docker, and backed by SQLite; enabling data sharing across maritime logistics actors including ports, operators and research partners.
+- **1L:** Built Express.js/Docker microservices for MISSION — Fraunhofer maritime data exchange research platform.
+
+**Key skills:** Express.js, JavaScript, Docker, SQLite, microservices, REST APIs, research prototyping
+**ATS keywords:** microservices, Docker, REST, Express.js, JavaScript, containerization
+**Reframing notes:**
+- Staff/Senior DE: frame as microservice/Docker evidence from pre-Bosch period
+- ML/AI: LOW — omit
+- Data Platform/Infra: include for early Docker/microservice signal
+
+---
+
+### Achievement FC-4: Predictive Maintenance Research Grant Contribution
+
+**Source:** thiessen_zeugnis_fraunhofer.md
+**User's role:** Contributor ("Mitarbeit" = participated)
+**Status:** Grant proposal (not an outcome claim)
+
+**Context:** Contributed to a Fraunhofer research grant proposal targeting ML-based prediction of optimal maintenance timing for maritime equipment.
+
+**Bullet variants:**
+- **1L:** Contributed to Fraunhofer research grant proposal for ML-based predictive maintenance in maritime operations.
+
+**Key skills:** ML, predictive maintenance, research grant writing, maritime domain
+**ATS keywords:** predictive maintenance, machine learning, research
+**Reframing notes:**
+- ML/AI: include as minor signal if space allows
+- All others: LOW — omit; rarely warrants a dedicated bullet on resume
+
+---
+
+## Position Summary
+
+| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
+|-------------|----|-----------------|-----------------------|--------------------|----------------------|
+| SCEDAS + CI/CD | FC-1 | HIGH | LOW | LOW | MED |
+| ARTUS ML/NLP | FC-2 | MED | LOW | HIGH | LOW |
+| MISSION Microservices | FC-3 | MED | LOW | MED | MED |
+| Predictive Maintenance Grant | FC-4 | LOW | LOW | MED | LOW |

resume_builder/experience/experience_generali.md

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+# Experience: Software Engineer → IT Consultant — Generali Deutschland Informatik Services GmbH (GDIS)
+## May 2015 – June 2017 | Hamburg/Cologne, Germany
+
+### Cross-Position Section
+
+**Career arc framing:** Generali (GDIS) was Dennis's first extended software role post-Bundeswehr — starting as a Graduate Trainee and progressing to IT Consultant in 9 months. He introduced BDD to the team (ran the initial PoC), held technical responsibility for test automation in a major workflow project, and pioneered Robotic Process Automation with UIPath. Also contributed to Apache Camel / Spring Boot integration PoC. Two-phase career: Graduate Programme (May 2015 – Sep 2016) then permanent IT Consultant role (Oct 2016 – Jun 2017). The Zeugnis rates performance as "gut" (good); employer regrets departure.
+
+**CL framing:** "At Generali's IT subsidiary, I went from Graduate Trainee to IT Consultant within 9 months. I introduced BDD to the team — running the initial PoC, presenting to the Java Community, and training colleagues — and held technical ownership of the BDD test automation for the PIA-Postkorb/workflow project. I also built the first UIPath RPA PoC at GDIS, demonstrating initiative to extend the team's automation toolset beyond BDD."
+
+---
+
+### Achievement GN-1: BDD Technical Ownership & Team Evangelism
+
+**Source:** thiessen_zeugnis_generali.md, thiessen_linkedin_profile.md
+**User's role:** Technical owner ("technische Verantwortung" — confirmed by Zeugnis)
+**Status:** Deployed / operational
+
+**Context:** Generali had no BDD practice. Dennis introduced BDD to the team, ran an initial PoC, took technical ownership of BDD test automation for the PIA-Postkorb/SE-Projekt Workflow, trained colleagues, presented to the Java Community, and administered Jenkins build jobs.
+
+**Bullet variants:**
+- **2L:** Introduced and held technical ownership of BDD test automation at Generali GDIS (Serenity-BDD, Selenium, JBehave), including PoC, Jenkins CI/CD administration, team training and knowledge transfer across the Java Community.
+- **3L:** Pioneered BDD (Behaviour-Driven Development) at Generali GDIS — designed and ran the initial PoC, then assumed technical ownership of the full BDD test automation suite for the PIA-Postkorb/SE-Projekt Workflow using Serenity-BDD, Selenium, and JBehave; administered Jenkins build jobs, presented BDD to the Java Community, trained project team members, and advised business units on BDD adoption — elevating test automation maturity across the department.
+- **1L:** Introduced BDD to Generali GDIS; held technical ownership of Serenity-BDD/Selenium/JBehave suite and Jenkins CI/CD.
+
+**Key skills:** BDD, Serenity-BDD, Selenium, JBehave, Jenkins, test automation, knowledge transfer, technical leadership, TDD
+**ATS keywords:** BDD, Selenium, Jenkins, test automation, CI/CD, JBehave, Java
+**Reframing notes:**
+- Staff/Senior DE: frame as initiative + technical ownership signal; not core DE work but shows leadership
+- ML/AI: LOW — omit
+- All roles: useful as "introduced a practice" signal — shows initiative and cross-team influence
+
+---
+
+### Achievement GN-2: RPA / UIPath POC Development
+
+**Source:** thiessen_zeugnis_generali.md, thiessen_linkedin_profile.md
+**User's role:** Primary developer ("Entwicklung von POCs mit UIPath" — confirmed Zeugnis)
+**Status:** Proof of concept
+
+**Context:** Dennis developed UIPath RPA POCs at Generali GDIS, extending automation beyond test tooling into business process automation. Also served as point of contact for RPA/UIPath to Generali group companies.
+
+**Bullet variants:**
+- **2L:** Developed UIPath RPA proofs of concept at Generali GDIS and served as internal point of contact for RPA adoption across Generali group companies — extending automation from test tooling into business process automation.
+- **1L:** Developed UIPath RPA POCs; internal RPA contact for Generali group companies.
+
+**Key skills:** UIPath, RPA, Robotic Process Automation, Camunda BPMN, business process automation
+**ATS keywords:** UIPath, RPA, Robotic Process Automation, business process automation
+**Reframing notes:**
+- Staff/Senior DE: LOW — include only if JD asks for RPA or process automation breadth
+- Platform/Infra: LOW — omit
+- Any role: niche signal; include if JD targets automation broadly; otherwise omit from resume, include in CV
+
+---
+
+### Achievement GN-3: Java/J2EE Application Development
+
+**Source:** thiessen_zeugnis_generali.md, thiessen_cv_master_profile.md
+**User's role:** Developer
+**Status:** Deployed / shipped
+
+**Context:** Developed application features in Java/J2EE for PIA-Postkorb/SE-Projekt Workflow, implemented new requirements, and fixed bugs. Migrated WebServices to XLDeploy deployment process. Contributed to Apache Camel + Spring Boot Dispatcher POC.
+
+**Bullet variants:**
+- **2L:** Developed Java/J2EE application features for the PIA-Postkorb workflow portal; migrated WebServices to XLDeploy deployment process and contributed to an Apache Camel / Spring Boot integration PoC.
+- **1L:** Developed Java/J2EE features for PIA-Postkorb workflow; migrated WebServices to XLDeploy; contributed Apache Camel/Spring Boot PoC.
+
+**Key skills:** Java, J2EE, JavaScript, Spring Boot, Apache Camel, XLDeploy, Oracle DB, web application development
+**ATS keywords:** Java, J2EE, Spring Boot, Apache Camel, Oracle DB
+**Reframing notes:**
+- All DE roles: LOW — Java/J2EE is legacy context; fold into skills if needed; rarely a standalone bullet at this career stage
+- Include only if JD explicitly requires Java backend or if filling page space on 2-page resume
+
+---
+
+### Achievement GN-4: IBM ODM Evaluation (Trainee Phase)
+
+**Source:** thiessen_zeugnis_generali.md
+**User's role:** Evaluator / technical analyst
+**Status:** Internal evaluation (Trainee program)
+
+**Context:** During the Graduate Trainee program, Dennis evaluated IBM Operation Decision Management (ODM) Decision Center v8.7 — a rules engine / decision management platform.
+
+**Bullet variants:**
+- **1L:** Evaluated IBM ODM (Operation Decision Management) Decision Center v8.7 as part of Graduate Trainee program.
+
+**Key skills:** IBM ODM, rules engine, decision management, IT consulting
+**ATS keywords:** IBM ODM, decision management, rules engine
+**Reframing notes:**
+- All roles: LOW — niche, legacy; include on CV only if targeting InsurTech or IBM-ecosystem roles
+- Omit from resume
+
+---
+
+## Position Summary
+
+| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
+|-------------|----|-----------------|-----------------------|--------------------|----------------------|
+| BDD Technical Ownership | GN-1 | MED | LOW | LOW | LOW |
+| UIPath RPA POC | GN-2 | LOW | LOW | LOW | LOW |
+| Java/J2EE App Dev | GN-3 | LOW | LOW | LOW | LOW |
+| IBM ODM Evaluation | GN-4 | LOW | LOW | LOW | LOW |
+
+**Note:** Generali is the 5th position on a 2-page resume. Typically appears as 1–2 condensed bullets. Recommend: GN-1 as the primary bullet (BDD ownership + initiative), optionally fold GN-2 (RPA) as a clause within. GN-3 and GN-4 only for full CV.

resume_builder/experience/experience_swisscom.md

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+# Experience: Staff Data, Analytics & AI Engineer — Swisscom (Schweiz) AG
+## October 2023 – Present | Bern, Switzerland
+
+### Cross-Position Section
+
+**Career arc framing:** Swisscom is Dennis's current and most senior role — a promotion from Senior to Staff (Engineer IV) in April 2025. This is the anchor position for all target role types. It demonstrates the full stack: owned pipelines, cloud migration, containerized delivery, security ownership, and stakeholder-facing data products. At a major national telco operating AWS-heavy infrastructure, this is the clearest signal for Staff/Senior Data Engineering, Data Platform, and ML Engineering roles.
+
+**CL framing (for cover letters):** "My current role at Swisscom — Switzerland's largest telco — gives me end-to-end ownership of business-critical data pipelines at scale: from Oracle and Kafka ingestion through Teradata DWH to AWS cloud-native architecture. I've led the migration of legacy pipelines to serverless AWS services and own the full DevOps lifecycle including Kubernetes deployment, GitLab CI/CD, and on-call support."
+
+---
+
+### Achievement SW-1: AWS Migration of Legacy ETL Stack
+
+**Source:** thiessen_swisscom_zwischenzeugnis.md, thiessen_cv_master_profile.md
+**User's role:** Primary owner / sole technical lead
+**Status:** Active / ongoing operational achievement
+
+**Context:** Legacy ETL pipelines ran on Teradata and Oracle. Migration to AWS cloud-native stack reduces operational overhead, improves scalability, and positions the team for modern serverless workflows.
+
+**Bullet variants:**
+- **2L:** Migrated legacy Teradata/Oracle ETL pipelines to AWS cloud-native architecture (S3, Glue, Athena with Apache Iceberg, Redshift, Airflow, CloudFormation), reducing manual operational overhead and enabling scalable, serverless data processing for downstream analytics.
+- **3L:** Led migration of legacy Teradata/Oracle ETL stack to a fully cloud-native AWS architecture using S3, Glue Jobs and Tables, Athena with Apache Iceberg (open table format), Redshift, Lambda, Step Functions, Airflow, and CloudFormation for IaC; reduced operational overhead, improved pipeline observability, and enabled scalable serverless processing — directly accelerating data availability for B2B stakeholder analytics.
+- **1L:** Migrated legacy ETL stack to AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation) for scalable serverless data processing.
+
+**Key skills:** AWS, S3, Glue, Athena, Apache Iceberg, Redshift, Lambda, Step Functions, Airflow, CloudFormation, IaC, ETL migration, cloud-native architecture
+**ATS keywords:** AWS, data pipeline migration, ETL, serverless, Airflow, Redshift, Glue, Athena, Apache Iceberg, CloudFormation, IaC
+**Reframing notes:**
+- Data Platform/Infra: lead with AWS architecture and serverless; de-emphasize downstream analytics angle
+- Staff/Senior DE: lead with ownership and scale; emphasize reduction in operational overhead
+- Analytics Engineer: lead with enabling analytics outcomes for B2B stakeholders
+- ML/AI: minor relevance — mention as infrastructure enabling ML data access
+
+---
+
+### Achievement SW-2: Component Ownership — Fulfillment ETL Pipelines
+
+**Source:** thiessen_swisscom_zwischenzeugnis.md, thiessen_cv_master_profile.md
+**User's role:** Component Owner — primary responsible engineer
+**Status:** Active / ongoing
+
+**Context:** Business-critical Fulfillment domain data flows from Oracle source systems into Teradata DWH via Kafka and Python pipelines. Dennis is Component Owner — accountable for data availability, SLA, quality, compliance and on-call duty.
+
+**Bullet variants:**
+- **2L:** Served as Component Owner for business-critical Fulfillment ETL pipelines (Oracle → Kafka → Teradata DWH in Python), ensuring data availability for downstream analysis under on-call SLA and full Data Governance compliance.
+- **3L:** Owned end-to-end component responsibility for Swisscom's Fulfillment domain ETL pipelines — ingesting business-critical data from Oracle and Kafka sources into Teradata DWH via Python; enforced Data Governance, security, and privacy standards; covered 2nd/3rd-level support and on-call duty to maintain SLA adherence at scale.
+- **1L:** Owned Fulfillment ETL pipelines (Oracle/Kafka → Teradata) as Component Owner under full on-call SLA and compliance accountability.
+
+**Key skills:** ETL/ELT, Python, Kafka, Oracle, Teradata DWH, data governance, component ownership, on-call SLA, SAP BODS
+**ATS keywords:** ETL, Kafka, Teradata, Oracle, data pipeline, data governance, SLA, component ownership
+**Reframing notes:**
+- Staff/Senior DE: this is the flagship ownership bullet — always include; leads with accountability signal
+- Data Platform/Infra: de-emphasize "Fulfillment domain" context; emphasize Kafka and Teradata scale
+- Analytics Engineer: frame around "enabling data availability for downstream analytics"
+- ML/AI: minor — mention as reliable data feed for ML models if needed
+
+---
+
+### Achievement SW-3: Python Applications on Kubernetes + GitLab CI/CD
+
+**Source:** thiessen_swisscom_zwischenzeugnis.md, thiessen_linkedin_profile.md
+**User's role:** Primary developer / operator
+**Status:** Active / ongoing
+
+**Context:** Python data applications deployed on Kubernetes clusters with GitLab CI/CD automation — containerized delivery in an agile DevOps team with full lifecycle ownership.
+
+**Bullet variants:**
+- **2L:** Designed, deployed and operated Python data applications on Kubernetes clusters with GitLab CI/CD automation, enabling reliable containerized pipeline delivery and continuous integration in an agile DevOps team.
+- **3L:** Built and operated Python-based data applications deployed to Kubernetes clusters; automated the full CI/CD lifecycle via GitLab, including build, test, and deployment pipelines — delivering containerized services reliably in an agile DevOps team with GitLab-managed quality gates and rollback controls.
+- **1L:** Deployed and operated Python data apps on Kubernetes with GitLab CI/CD in an agile DevOps team.
+
+**Key skills:** Python, Kubernetes, GitLab CI/CD, Docker, containerization, DevOps, agile
+**ATS keywords:** Kubernetes, Python, GitLab, CI/CD, Docker, DevOps, containerization
+**Reframing notes:**
+- Data Platform/Infra: lead with K8s and CI/CD; emphasize infrastructure automation angle
+- Staff/Senior DE: pair with SW-2 to show pipeline + deployment ownership as a unit
+- ML/AI: frame as "deployed ML-ready Python services to Kubernetes"
+
+---
+
+### Achievement SW-4: B2B Data Products, Stakeholder Analytics & Process Automation
+
+**Source:** thiessen_cv_master_profile.md, thiessen_swisscom_zwischenzeugnis.md, thiessen_linkedin_profile.md
+**User's role:** Data product owner / analyst-engineer interface
+**Status:** Active / ongoing
+
+**Context:** Delivered data products, dashboards and analyses for B2B stakeholders; also drove automation of technical processes and conducted root cause analysis under 2nd/3rd level support.
+
+**Bullet variants:**
+- **2L:** Delivered data products, analyses and dashboards for B2B stakeholders; drove automation of technical workflows and performed root cause analysis under 2nd/3rd-level support responsibility to maintain data platform reliability.
+- **3L:** Partnered with Product Owner to refine and prioritize backlog, enabling agile delivery of data products and dashboards for B2B stakeholders; proactively drove automation of recurring technical processes and conducted structured root cause analysis under 2nd/3rd-level support and on-call duty — bridging engineering depth with business delivery cadence.
+- **1L:** Delivered B2B data products and dashboards; drove process automation and root cause analysis under 3rd-level support.
+
+**Key skills:** Data products, dashboards, stakeholder management, root cause analysis, agile backlog management, product ownership collaboration, PySpark
+**ATS keywords:** data products, stakeholder management, agile, backlog, dashboards, root cause analysis
+**Reframing notes:**
+- Analytics Engineer: this is the primary bullet for this role type — lead with stakeholder/product angle
+- Staff/Senior DE: supporting bullet; frame around reliability and automation
+- ML/AI: minor relevance unless JD asks for MLOps/data product ownership
+
+---
+
+### Achievement SW-5: Security Champion — 3 Consecutive Years
+
+**Source:** thiessen_swisscom_security_champion.md, thiessen_swisscom_zwischenzeugnis.md
+**User's role:** Designated Security Champion (annually renewed)
+**Status:** Active (2025/26 badge current)
+
+**Context:** Swisscom's Security Champion program requires 100h of structured training covering Cloud Security, DevSecOps, Security by Design, and Risk Management, plus a 40-question assessment (>80% passing grade). Dennis has held this role for 3 consecutive years.
+
+**Bullet variants:**
+- **2L:** Named Swisscom Security Champion for 3 consecutive years (2023/24–2025/26), owning security compliance, risk monitoring and deviation tracking for the team's pipelines; completed 100h annual DevSecOps training with >80% assessment score.
+- **3L:** Designated as Security Champion for Swisscom's Data Lake team for 3 consecutive years (2023/24, 2024/25, 2025/26) — responsible for security compliance in development and operation, risk monitoring, and deviation reporting; fulfilled annual 100h structured training across Cloud Security, DevSecOps, Security by Design, and Security Risk Management, passing a 40-question comprehensive assessment with >80% score each year.
+- **1L:** Swisscom Security Champion for 3 consecutive years (2023–2026) — DevSecOps, risk monitoring, 100h training + assessment.
+
+**Key skills:** DevSecOps, security compliance, risk management, security awareness, Security by Design
+**ATS keywords:** DevSecOps, security champion, security compliance, risk management, cloud security
+**Reframing notes:**
+- Data Platform/Infra: HIGH relevance — embed security in infrastructure angle
+- Staff/Senior DE: include as supporting signal for senior-level ownership breadth
+- Analytics Engineer: LOW — de-emphasize or omit unless JD asks for security awareness
+- ML/AI: include for AI-adjacent roles where model security/compliance is relevant
+
+---
+
+### Achievement SW-6: PySpark Backend Engineering
+
+**Source:** thiessen_linkedin_profile.md
+**User's role:** Developer
+**Status:** Active / ongoing (Staff-level confirmed)
+
+**Context:** PySpark used in backend data engineering at Staff level at Swisscom. Confirms Big Data processing capability beyond standard Python/SQL.
+
+**Bullet variants:**
+- **2L:** Applied PySpark for large-scale backend data processing alongside Python and SQL, extending pipeline capabilities to distributed Big Data workloads within the Swisscom Data Lake platform.
+- **1L:** Applied PySpark for distributed data processing in the Swisscom Data Lake environment.
+
+**Key skills:** PySpark, Apache Spark, big data, distributed computing
+**ATS keywords:** PySpark, Spark, big data, distributed processing
+**Reframing notes:**
+- This is a skills signal more than a standalone achievement; roll into skills taxonomy
+- Mention in bullet if JD explicitly requires Spark/PySpark
+- Can be folded into SW-2 or SW-3 bullet if space is tight
+
+---
+
+## Position Summary
+
+| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
+|-------------|----|-----------------|-----------------------|--------------------|----------------------|
+| AWS Migration | SW-1 | HIGH | HIGH | MED | HIGH |
+| Component Owner / Fulfillment ETL | SW-2 | HIGH | HIGH | MED | HIGH |
+| Kubernetes + GitLab CI/CD | SW-3 | HIGH | MED | HIGH | HIGH |
+| B2B Data Products + Automation | SW-4 | MED | HIGH | MED | MED |
+| Security Champion | SW-5 | MED | LOW | MED | HIGH |
+| PySpark | SW-6 | MED | LOW | MED | MED |

resume_builder/experience/experience_vizrt.md

@@ -0,0 +1,67 @@
+# Experience: DevOps Engineer — Vizrt
+## July 2017 – May 2018 | Bergen, Norway
+
+### Cross-Position Section
+
+**Career arc framing:** Vizrt was Dennis's only international role outside DACH — a Norwegian broadcast technology company (customers: CNN, BBC, Al Jazeera). He worked embedded in the Coder (software engineering) team, not a standalone QA/test team — his scope covered Python/C++ backend development, automated test suite development, and CI/CD pipeline integration with quality gates. The DevOps title reflects this full scope: engineering + test automation integrated into the delivery pipeline. The reference from Team Lead Raymond Hilseth explicitly states he "exceeded expectations" with "little to no supervision." Resigned voluntarily to return to Germany.
+
+**Title flexibility:** Use "DevOps Engineer" as primary. For roles where CI/CD is the key signal, emphasize pipeline integration; for roles where software engineering is primary, emphasize backend development. Do NOT reduce to "Test Automation Engineer" — the role was broader.
+
+**CL framing:** "At Vizrt in Bergen, I worked as a DevOps engineer embedded in the core software team — developing distributed video transcoding backend components in Python and C++, building the automated test suite for A/V streaming, and integrating tests and quality gates into the CI/CD pipeline. The reference from my team lead confirms I operated with minimal supervision and exceeded expectations throughout."
+
+---
+
+### Achievement VZ-1: Distributed Video Transcoding Backend
+
+**Source:** thiessen_cv_master_profile.md
+**User's role:** Primary developer
+**Status:** Shipped to production
+
+**Context:** Vizrt's broadcast software requires a distributed backend for real-time video transcoding. Dennis engineered backend components in Python and C++ as part of the Coder team — contributing to the core transcoding pipeline used by global broadcast customers.
+
+**Bullet variants:**
+- **2L:** Engineered distributed video transcoding backend components in Python and C++ for Vizrt's broadcast platform (customers: CNN, BBC, Al Jazeera), contributing to the core A/V processing pipeline as part of the software engineering team.
+- **3L:** Developed Python and C++ backend components for Vizrt's distributed real-time video transcoding system, used in live broadcast workflows by major global media organizations including CNN, BBC, and Al Jazeera; contributed to the core A/V pipeline within the Coder (software engineering) team, operating with high autonomy under minimal supervision.
+- **1L:** Developed Python/C++ distributed video transcoding backend for Vizrt broadcast platform (CNN, BBC, Al Jazeera).
+
+**Key skills:** Python, C++, distributed systems, A/V streaming, backend engineering
+**ATS keywords:** Python, C++, distributed systems, backend engineering, streaming
+**Reframing notes:**
+- Staff/Senior DE: include as Python/C++ breadth signal; frame around distributed systems
+- ML/AI: LOW — omit or condense
+- Platform/Infra: include as distributed backend signal
+- All roles: customer name-dropping (CNN, BBC) adds credibility to the role's scale
+
+---
+
+### Achievement VZ-2: Test Automation + CI/CD Quality Gates Integration
+
+**Source:** thiessen_cv_master_profile.md, thiessen_zeugnis_vizrt.md, user clarification
+**User's role:** Primary developer
+**Status:** Deployed / operational
+
+**Context:** Dennis built integration and unit test suites for Audio, Video & Streaming in Python AND integrated them into the CI/CD pipeline with quality gates — ensuring automated checks ran on every build and blocked releases that failed quality thresholds. This is the DevOps angle: not just writing tests, but owning the quality gate mechanism in the delivery pipeline.
+
+**Bullet variants:**
+- **2L:** Built automated integration and unit test suite for A/V streaming (Python) and integrated quality gates into the CI/CD pipeline, shortening the feedback loop for new features and bug fixes while improving release quality of Vizrt's broadcast software.
+- **3L:** Developed a comprehensive automated test suite (integration and unit tests in Python) for Vizrt's Audio, Video and Streaming components, and integrated these tests as quality gates into the CI/CD delivery pipeline — blocking failing builds, reducing time to market, and improving release-over-release reliability of broadcast software deployed to global media customers including CNN, BBC, and Al Jazeera.
+- **1L:** Built Python A/V test suite and integrated quality gates into CI/CD pipeline for Vizrt broadcast platform.
+
+**Key skills:** Python, test automation, integration testing, unit testing, CI/CD, quality gates, A/V streaming, DevOps
+**ATS keywords:** test automation, Python, CI/CD, quality gates, integration testing, DevOps
+**Reframing notes:**
+- Staff/Senior DE: pair with VZ-1; frame as "DevOps ownership of test pipeline"
+- Data Platform/Infra: include for CI/CD quality gate signal
+- ML/AI: LOW — omit
+- All roles: CI/CD integration angle elevates this beyond pure test writing — always use this framing
+
+---
+
+## Position Summary
+
+| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
+|-------------|----|-----------------|-----------------------|--------------------|----------------------|
+| Video Transcoding Backend | VZ-1 | MED | LOW | LOW | MED |
+| Automated Test Suite | VZ-2 | MED | LOW | LOW | LOW |
+
+**Note:** Vizrt is typically the 4th position on a 2-page resume and may appear as a condensed single bullet combining both achievements. Use 2L variants when space allows; condense to 1L if page-limited.