# 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 Analytics Platform Ownership + TAF 2022 Talk

**Source:** thiessen_linkedin_profile.md; TIBCO Analytics Forum 2022 article (community.spotfire.com/articles/spotfire/tibco-analytics-forum-2022/) — VERIFIED 2026-06-15 via Playwright scrape. Name spelled "Dennis Thießen" (eszett) on the source.
**User's role:** Co-owner of Spotfire applications/platform + developer of C# extensions; conference co-presenter
**Status:** Deployed / operational; publicly documented speaking credential

**Context:** At Bosch, Dennis co-owned the TIBCO Spotfire analytics platform that served semiconductor/fab engineers (alongside the Defect Management System he was Application Owner of — see BS-3), and built C# extensions to extend its visualization/analysis capabilities. His official TAF 2022 speaker bio states his responsibilities included "ownership of Spotfire applications and the semiconductor-specific Defect Management System... and the evaluation and integration of machine learning components for 24/7 use." He **co-presented at the TIBCO Analytics Forum (TAF) 2022**: session "Understanding Spatial Variability in Semiconductor Products with Spotfire Map Charts" (with Mark Herrmann) — multi-layered/trellised wafer-map charts and custom smoothed 2-D contour plots used by engineers to understand process/product variability and drive product quality and reliability across Bosch development and manufacturing groups.

**Bullet variants:**
- **2L:** Co-owned Bosch's TIBCO Spotfire analytics platform serving semiconductor engineers, building C# extensions and custom map/contour visualizations for wafer spatial data; presented this work at the TIBCO Analytics Forum 2022.
- **1L:** Co-owned the TIBCO Spotfire analytics platform and built C# extensions; presented at TIBCO Analytics Forum 2022.

**Speaking credential (for CL / presentations / honors):** Co-presenter, TIBCO Analytics Forum (TAF) 2022 — "Understanding Spatial Variability in Semiconductor Products with Spotfire Map Charts."

**Key skills:** TIBCO Spotfire, C#, BI platform ownership, data visualization, wafer-map/spatial analytics, data quality & reliability
**ATS keywords:** TIBCO Spotfire, C#, BI, data visualization, analytics platform, data quality
**Reframing notes:**
- Analytics Engineer / Data Engineer (BI-bridged JDs): NOW MED-HIGH — named, owned BI platform + a public speaking credential. Combine with BS-3 (Application Owner) into one strong "analytics platform ownership" bullet; surface TAF 2022 talk in the cover letter or as an interview bridge.
- ML/AI, Platform: LOW — omit unless BI/analytics is in the JD.

---

## 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 |
