14 KiB
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
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: Primary developer / current Staff-level focus area Status: Active / ongoing (current emphasis)
Context: Current Staff-level work building decentralized Data Mesh architecture, reusable data products, and active metadata management on AWS (Glue, Athena, CloudFormation, AWS CLI, CI/CD deployments). This is the governed, discoverable data foundation that downstream AI and agentic workflows depend on — enabling "AI speak-to-data" / grounded retrieval over enterprise data. Directly maps to agentic reference-architecture and MCP-based tool/data-access requirements.
Bullet variants:
- 2L: Built decentralized Data Mesh and reusable data products with active metadata management on AWS (Glue, Athena, CloudFormation, CI/CD) — the governed, discoverable data foundation that downstream AI and agentic workflows query directly.
- 3L: Architected decentralized Data Mesh with reusable, governed data products and active metadata management on AWS (Glue, Athena, CloudFormation, AWS CLI, automated CI/CD) — establishing the discoverable, well-described data foundation that downstream AI and agentic workflows depend on for grounded, "speak-to-data" retrieval over enterprise sources.
- 1L: Built AWS Data Mesh, data products and metadata management — the governed data foundation for downstream AI/agentic workflows.
Key skills: Data Mesh, data products, metadata management, 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 (agentic): HIGH — lead bridge to "reference architecture for agentic systems" + "grounded retrieval / MCP tool-data access"; frame data layer as what agents query
- Data Platform/Infra: HIGH — Data Mesh + metadata + AWS IaC is core platform signal
- Staff/Senior DE: HIGH — decentralized architecture ownership at scale
- Analytics Engineer: MED — data products enable self-serve analytics
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 |
| Data Mesh / Data Products / Metadata (agentic foundation) | SW-7 | HIGH | MED | HIGH | HIGH |