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claude-resume-kit/resume_builder/experience/experience_swisscom.md
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dennisthiessenandClaude Opus 5 f1095a062f feat(applications): archive Aker BP package, add Compass evidence, extend scout 
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>
2026-08-01 10:18:07 +02:00

17 KiB
Raw Blame History

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 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.

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."

Achievement SW-1: AWS Migration of Legacy ETL Stack

Source: thiessen_swisscom_zwischenzeugnis.md, thiessen_cv_master_profile.md 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. Status: Active / ongoing operational achievement

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.

Bullet variants: (scope-corrected 2026-07-27 — object must be his domains' pipelines, never "the" company stack)

  • 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.
  • 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.
  • 1L: Migrated his domains' ETL pipelines to AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation) for serverless processing.

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]].

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 — 2025/2026 (team role, NOT an award)

Source: thiessen_swisscom_security_champion.md, thiessen_swisscom_zwischenzeugnis.md User's role: Designated Security Champion — a mandatory team role (security point of contact), not an award or honor Status: Active — 2025/2026 only

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]].

Default action: OMIT from resume and CV. Include only when the JD explicitly requires security or DevSecOps experience. Never list under Awards/Honors.

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).

Bullet variants: (use ONLY if the JD explicitly asks for security/DevSecOps)

  • 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.
  • 1L: Team Security Champion (2025/2026) — 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: LOW by default; include only when the JD explicitly requires security or DevSecOps exposure
  • Staff/Senior DE: LOW by default; do not use as a generic seniority signal
  • Analytics Engineer: LOW — de-emphasize or omit unless JD asks for security awareness
  • ML/AI: include only when the JD explicitly asks for security/compliance; this is not responsible-AI ownership

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

Achievement SW-7: Data Mesh, Data Products & Metadata Management (AWS) — Foundation for Agentic AI

Source: User-verified current work (2026), thiessen_cv_master_profile.md (AWS stack) 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. Status: Active / ongoing (current emphasis)

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.

Bullet variants:

  • 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.
  • 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.
  • 1L: Build governed data products and metadata within Swisscom's company-wide AWS Data Mesh.

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.

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 ATS keywords: Data Mesh, data products, metadata management, AWS, Glue, Athena, CloudFormation, CI/CD, data governance, grounded retrieval, agentic AI foundation Reframing notes:

  • ML/AI: MED — evidence for data readiness and grounded enterprise data, not agent architecture or retrieval ownership
  • Data Platform/Infra: HIGH — Data Mesh + metadata + AWS IaC is core platform signal
  • Staff/Senior DE: HIGH — governed data-product delivery within a company-wide architecture
  • Analytics Engineer: MED — data products enable self-serve analytics

Achievement SW-8: Domain-Grounded LLM Agents for Q&A and Task Assistance

Source: User-confirmed current Swisscom work (2026-07-10) User's role: Creator / configurator of the agents in a Swisscom-owned web interface 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.

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.

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.

Key skills: LLM application configuration, model selection, AI agents, domain knowledge bases, question answering, migration assistance, data mapping ATS keywords: LLM-powered applications, AI agents, model selection, knowledge grounding, question answering, workflow automation, data mapping Reframing notes:

  • ML/AI: HIGH for LLM-specific roles; lead with applied LLM delivery, then connect reliable data foundations and evaluation discipline.
  • Accuracy: Never say “trained an LLM” unless model weights were fine-tuned. Prefer “grounded,” “configured,” or “provided with a domain-specific knowledge base.”
  • Production: Use “internal” or “prototype” only if true; otherwise omit deployment-status language until verified.

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 LOW LOW LOW LOW
PySpark SW-6 MED LOW MED MED
Data Mesh / Data Products / Metadata (agentic foundation) SW-7 HIGH MED HIGH HIGH
Domain-Grounded LLM Agents SW-8 MED LOW HIGH MED
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