dennisthiessenandClaude Opus 5
f1095a062f
Aker BP ASA — Data Product Architect (FINN 469067315): archive the finalized resume/CL sources, JD, session and critique. Submitted 2026-07-30 ahead of the 2026-08-02 deadline; logged as applied in the scout decision log and marked SUBMITTED in Active Sessions. KB: record Atlassian Compass as Swisscom's metadata/catalogue platform for data products and lineage (user-confirmed 2026-07-29). Practitioner use only — claims.json forbids claiming administration, rollout or ownership, and it is explicitly not a substitute claim for Purview, Collibra or Alation. Also: close out the Google Business Home session (no interview), extend the job scout, and allow the job-board domains used during the Aker BP research. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
207 lines
17 KiB
Markdown
207 lines
17 KiB
Markdown
# Experience: Staff Data, Analytics & AI Engineer — Swisscom (Schweiz) AG
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## October 2023 – Present | Bern, Switzerland
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### Cross-Position Section
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**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 owned pipeline components, scoped cloud-migration delivery, containerized operation and stakeholder-facing data products. The 2025/2026 Security Champion assignment is a secondary team role, not a core ownership claim.
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**CL framing (for cover letters):** "At Swisscom I own business-critical pipeline components in the Fulfillment domain, from Oracle and Kafka ingestion through production support. I migrated my domains' pipelines onto the company's AWS platform and build governed data products within its wider Data Mesh, while contributing to the broader migration programme."
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---
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### Achievement SW-1: AWS Migration of Legacy ETL Stack
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**Source:** thiessen_swisscom_zwischenzeugnis.md, thiessen_cv_master_profile.md
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**User's role:** Primary engineer for the migration of **his own domains'** pipelines; contributor to the wider company migration programme. **NOT a solo lead** — user-corrected 2026-07-27.
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**Status:** Active / ongoing operational achievement
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**Context:** Legacy ETL pipelines ran on Teradata and Oracle. Dennis implemented migration work for pipelines in his own domains using the company's AWS platform. No cost, scale or time-saving metric has been verified.
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**Bullet variants:** (scope-corrected 2026-07-27 — object must be **his domains'** pipelines, never "the" company stack)
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- **2L:** Migrated his domains' 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.
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- **3L:** Migrated the Fulfillment and Product Analysis domains' legacy Teradata/Oracle ETL pipelines to a 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 — contributing to Swisscom's wider cloud migration programme.
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- **1L:** Migrated his domains' ETL pipelines to AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation) for serverless processing.
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**Overclaiming warning:** Do NOT write "Led migration of the legacy stack" or imply sole ownership of a company-wide migration. See CLAUDE.md Scope Discipline and `[[feedback_bigcorp_ownership_scope]]`.
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**Key skills:** AWS, S3, Glue, Athena, Apache Iceberg, Redshift, Lambda, Step Functions, Airflow, CloudFormation, IaC, ETL migration, cloud-native architecture
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**ATS keywords:** AWS, data pipeline migration, ETL, serverless, Airflow, Redshift, Glue, Athena, Apache Iceberg, CloudFormation, IaC
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**Reframing notes:**
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- Data Platform/Infra: lead with AWS architecture and serverless; de-emphasize downstream analytics angle
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- Staff/Senior DE: lead with ownership and scale; emphasize reduction in operational overhead
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- Analytics Engineer: lead with enabling analytics outcomes for B2B stakeholders
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- ML/AI: minor relevance — mention as infrastructure enabling ML data access
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---
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### Achievement SW-2: Component Ownership — Fulfillment ETL Pipelines
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**Source:** thiessen_swisscom_zwischenzeugnis.md, thiessen_cv_master_profile.md
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**User's role:** Component Owner — primary responsible engineer
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**Status:** Active / ongoing
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**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.
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**Bullet variants:**
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- **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.
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- **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.
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- **1L:** Owned Fulfillment ETL pipelines (Oracle/Kafka → Teradata) as Component Owner under full on-call SLA and compliance accountability.
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**Key skills:** ETL/ELT, Python, Kafka, Oracle, Teradata DWH, data governance, component ownership, on-call SLA, SAP BODS
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**ATS keywords:** ETL, Kafka, Teradata, Oracle, data pipeline, data governance, SLA, component ownership
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**Reframing notes:**
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- Staff/Senior DE: this is the flagship ownership bullet — always include; leads with accountability signal
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- Data Platform/Infra: de-emphasize "Fulfillment domain" context; emphasize Kafka and Teradata scale
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- Analytics Engineer: frame around "enabling data availability for downstream analytics"
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- ML/AI: minor — mention as reliable data feed for ML models if needed
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---
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### Achievement SW-3: Python Applications on Kubernetes + GitLab CI/CD
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**Source:** thiessen_swisscom_zwischenzeugnis.md, thiessen_linkedin_profile.md
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**User's role:** Primary developer / operator
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**Status:** Active / ongoing
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**Context:** Python data applications deployed on Kubernetes clusters with GitLab CI/CD automation — containerized delivery in an agile DevOps team with full lifecycle ownership.
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**Bullet variants:**
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- **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.
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- **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.
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- **1L:** Deployed and operated Python data apps on Kubernetes with GitLab CI/CD in an agile DevOps team.
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**Key skills:** Python, Kubernetes, GitLab CI/CD, Docker, containerization, DevOps, agile
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**ATS keywords:** Kubernetes, Python, GitLab, CI/CD, Docker, DevOps, containerization
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**Reframing notes:**
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- Data Platform/Infra: lead with K8s and CI/CD; emphasize infrastructure automation angle
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- Staff/Senior DE: pair with SW-2 to show pipeline + deployment ownership as a unit
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- ML/AI: frame as "deployed ML-ready Python services to Kubernetes"
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---
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### Achievement SW-4: B2B Data Products, Stakeholder Analytics & Process Automation
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**Source:** thiessen_cv_master_profile.md, thiessen_swisscom_zwischenzeugnis.md, thiessen_linkedin_profile.md
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**User's role:** Data product owner / analyst-engineer interface
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**Status:** Active / ongoing
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**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.
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**Bullet variants:**
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- **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.
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- **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.
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- **1L:** Delivered B2B data products and dashboards; drove process automation and root cause analysis under 3rd-level support.
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**Key skills:** Data products, dashboards, stakeholder management, root cause analysis, agile backlog management, product ownership collaboration, PySpark
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**ATS keywords:** data products, stakeholder management, agile, backlog, dashboards, root cause analysis
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**Reframing notes:**
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- Analytics Engineer: this is the primary bullet for this role type — lead with stakeholder/product angle
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- Staff/Senior DE: supporting bullet; frame around reliability and automation
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- ML/AI: minor relevance unless JD asks for MLOps/data product ownership
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---
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### Achievement SW-5: Security Champion — 2025/2026 (team role, NOT an award)
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**Source:** thiessen_swisscom_security_champion.md, thiessen_swisscom_zwischenzeugnis.md
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**User's role:** Designated Security Champion — a mandatory team role (security point of contact), **not an award or honor**
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**Status:** Active — **2025/2026 only**
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> **CORRECTED 2026-07-27 (user-confirmed, second time).** This was previously written as "3 consecutive years (2023/24–2025/26)" — that is **wrong**. Dennis holds the badge for **2025/2026 only**. It is a rotating team role, not a distinction. See `config.md` KB Corrections and `[[feedback_security_champion]]`.
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>
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> **Default action: OMIT from resume and CV.** Include only when the JD explicitly requires security or DevSecOps experience. Never list under Awards/Honors.
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**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).
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**Bullet variants:** (use ONLY if the JD explicitly asks for security/DevSecOps)
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- **2L:** Serve as Security Champion for the team (2025/2026), covering security compliance, risk monitoring and deviation tracking for the team's pipelines; completed 100h DevSecOps training with >80% assessment score.
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- **1L:** Team Security Champion (2025/2026) — DevSecOps, risk monitoring, 100h training + assessment.
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**Key skills:** DevSecOps, security compliance, risk management, security awareness, Security by Design
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**ATS keywords:** DevSecOps, security champion, security compliance, risk management, cloud security
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**Reframing notes:**
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- Data Platform/Infra: LOW by default; include only when the JD explicitly requires security or DevSecOps exposure
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- Staff/Senior DE: LOW by default; do not use as a generic seniority signal
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- Analytics Engineer: LOW — de-emphasize or omit unless JD asks for security awareness
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- ML/AI: include only when the JD explicitly asks for security/compliance; this is not responsible-AI ownership
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---
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### Achievement SW-6: PySpark Backend Engineering
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**Source:** thiessen_linkedin_profile.md
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**User's role:** Developer
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**Status:** Active / ongoing (Staff-level confirmed)
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**Context:** PySpark used in backend data engineering at Staff level at Swisscom. Confirms Big Data processing capability beyond standard Python/SQL.
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**Bullet variants:**
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- **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.
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- **1L:** Applied PySpark for distributed data processing in the Swisscom Data Lake environment.
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**Key skills:** PySpark, Apache Spark, big data, distributed computing
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**ATS keywords:** PySpark, Spark, big data, distributed processing
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**Reframing notes:**
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- This is a skills signal more than a standalone achievement; roll into skills taxonomy
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- Mention in bullet if JD explicitly requires Spark/PySpark
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- Can be folded into SW-2 or SW-3 bullet if space is tight
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---
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### Achievement SW-7: Data Mesh, Data Products & Metadata Management (AWS) — Foundation for Agentic AI
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**Source:** User-verified current work (2026), thiessen_cv_master_profile.md (AWS stack)
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**User's role:** Builds and models governed data products and onboards sources within Swisscom's company-wide Data Mesh; does not own or architect the shared company-wide mesh.
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**Status:** Active / ongoing (current emphasis)
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**Context:** Current Staff-level work builds reusable governed **data products**, active metadata and source onboarding within Swisscom's shared Data Mesh on AWS (Glue, Athena, CloudFormation, AWS CLI, CI/CD). These products can support downstream analytics and AI use cases. Do not convert this into ownership of agent architecture, MCP tooling or the company-wide platform.
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**Bullet variants:**
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- **2L:** Build governed data products with active metadata management within Swisscom's company-wide Data Mesh on AWS (Glue, Athena, CloudFormation), supporting discoverable data access for analytics and AI use cases.
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- **3L:** Model and build governed data products, onboard source systems and maintain active metadata within Swisscom's company-wide Data Mesh on AWS (Glue, Athena, CloudFormation and CI/CD), giving downstream teams discoverable, well-described data without claiming ownership of the shared platform.
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- **1L:** Build governed data products and metadata within Swisscom's company-wide AWS Data Mesh.
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**Metadata/catalogue platform (user-confirmed 2026-07-29):** Swisscom uses **Atlassian Compass** as the metadata platform for data products, including lineage. Dennis uses it as a practitioner to document data-product metadata and lineage. **Do NOT claim** he administers, rolled out or owns Compass. This is a genuine named catalogue/lineage tool and satisfies "or similar metadata/catalogue platform" JD phrasing — but it is **not** a substitute claim for Purview, Collibra or Alation, which remain unevidenced and forbidden.
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**Key skills:** Data Mesh, data products, metadata management, data lineage, Atlassian Compass, data catalog, data governance, AWS, Glue, Athena, CloudFormation, AWS CLI, CI/CD, agentic data foundation, grounded retrieval
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**ATS keywords:** Data Mesh, data products, metadata management, AWS, Glue, Athena, CloudFormation, CI/CD, data governance, grounded retrieval, agentic AI foundation
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**Reframing notes:**
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- ML/AI: MED — evidence for data readiness and grounded enterprise data, not agent architecture or retrieval ownership
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- Data Platform/Infra: HIGH — Data Mesh + metadata + AWS IaC is core platform signal
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- Staff/Senior DE: HIGH — governed data-product delivery within a company-wide architecture
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- Analytics Engineer: MED — data products enable self-serve analytics
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---
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### Achievement SW-8: Domain-Grounded LLM Agents for Q&A and Task Assistance
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**Source:** User-confirmed current Swisscom work (2026-07-10)
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**User's role:** Creator / configurator of the agents in a Swisscom-owned web interface
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**Status:** Models are selectable in the web interface. Deployment, adoption, API and retrieval implementation are not known — do not call production deployment, fine-tuning, RAG, hybrid search, API engineering, or broader agentic-system ownership without further evidence.
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**Context:** Dennis configured LLM agents in a Swisscom-owned web interface, selecting from available models and supplying a domain-specific knowledge base for question answering and domain tasks, including migration assistance and data mapping. This is direct hands-on LLM application configuration, distinct from model training, fine-tuning or API-level deployment.
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**Safe bullet direction:** Configured domain-grounded LLM agents in a Swisscom-owned web interface, selecting available models and supplying a knowledge base for Q&A, migration assistance and data mapping. Add only verified adoption, API or retrieval details.
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**Key skills:** LLM application configuration, model selection, AI agents, domain knowledge bases, question answering, migration assistance, data mapping
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**ATS keywords:** LLM-powered applications, AI agents, model selection, knowledge grounding, question answering, workflow automation, data mapping
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**Reframing notes:**
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- ML/AI: HIGH for LLM-specific roles; lead with applied LLM delivery, then connect reliable data foundations and evaluation discipline.
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- Accuracy: Never say “trained an LLM” unless model weights were fine-tuned. Prefer “grounded,” “configured,” or “provided with a domain-specific knowledge base.”
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- Production: Use “internal” or “prototype” only if true; otherwise omit deployment-status language until verified.
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---
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## Position Summary
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| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
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|-------------|----|-----------------|-----------------------|--------------------|----------------------|
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| AWS Migration | SW-1 | HIGH | HIGH | MED | HIGH |
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| Component Owner / Fulfillment ETL | SW-2 | HIGH | HIGH | MED | HIGH |
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| Kubernetes + GitLab CI/CD | SW-3 | HIGH | MED | HIGH | HIGH |
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| B2B Data Products + Automation | SW-4 | MED | HIGH | MED | MED |
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| Security Champion | SW-5 | LOW | LOW | LOW | LOW |
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| PySpark | SW-6 | MED | LOW | MED | MED |
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| Data Mesh / Data Products / Metadata (agentic foundation) | SW-7 | HIGH | MED | HIGH | HIGH |
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| Domain-Grounded LLM Agents | SW-8 | MED | LOW | HIGH | MED |
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