# Bundle: Staff / Senior Data Engineer

> Target employers: Tech companies, scale-ups, platform teams
> Tier: 1 — strongest evidence, full portfolio
> Config key: bundle_data_engineer.md

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## S1: Role Profile & Priority Matrix

**Positioning:** Dennis is a Staff-level data engineer with 5+ years building production-grade ETL pipelines and data platforms at scale — from Oracle/Teradata DWH ownership at Swisscom (Switzerland's largest telco) to containerized ML inference in a 24/7 semiconductor fab at Bosch. His AWS certification (SAA, active), cloud migration ownership, and Kubernetes-based deployment experience position him as a senior-to-staff candidate across data engineering, data platform, and data infrastructure roles.

**Promotion signal to use:** "Promoted from Senior to Staff Engineer (Engineer IV) at Swisscom, April 2025."

### Priority Matrix

| Priority | Achievement IDs | Rationale |
|----------|----------------|-----------|
| HIGH | SW-2, SW-1, SW-3, BS-3, BS-1, BS-2 | Core DE ownership: component owner, AWS migration, K8s/CI/CD, app owner, ML pipelines, data services |
| MED | SW-4, SW-5, SW-6, BS-4, FC-1, VZ-2, GN-1 | Breadth signals: stakeholder products, DevSecOps, PySpark, ELK PoC, CI/CD, BDD ownership |
| LOW | FC-2, FC-3, VZ-1, GN-2, GN-3, CA-1 | Earlier career / non-core for this audience |

**2-page resume bullet allocation (typical):**
- Swisscom: 3–4 bullets (SW-1, SW-2, SW-3, SW-4 or SW-5)
- Bosch: 3 bullets (BS-1, BS-2, BS-3; +BS-4 if space)
- Fraunhofer: 1–2 bullets (FC-1 compressed)
- Vizrt: 1 bullet (VZ-1 + VZ-2 combined)
- Generali: 1 bullet (GN-1)

---

## S2: Summary Guide

**Headline pattern:**
> "Staff Data Engineer | AWS · Kafka · Kubernetes | ETL Pipelines, Cloud Migration & Production ML"

**Building blocks** (3–5 phrases that should appear in summaries for this role type):
- "end-to-end ETL pipeline ownership" or "component ownership of business-critical data pipelines"
- "cloud migration" or "legacy-to-AWS migration"
- "Kafka-based event-driven ingestion"
- "Kubernetes deployment" or "containerized data applications"
- "AWS Certified Solutions Architect" (cert signal)

**Tone:** Engineer who owns systems, not just builds them. Accountability + delivery. Operator mindset.

**Avoid:**
- Academic or research framing
- "Passionate about data" clichés
- Overemphasizing testing/QA background (earlier career)
- Listing every tool — focus on the stack that matters for the JD

---

## S3: Achievement Reframing Map

| ID | Default Framing | This Role's Framing | Key Metric / Signal |
|----|----------------|--------------------|--------------------|
| SW-2 | Component Owner, Fulfillment ETL | **Lead bullet** — "owned business-critical Fulfillment pipelines end-to-end, on-call SLA, Data Governance compliance" | Component Owner title, on-call accountability |
| SW-1 | AWS migration | "Migrated legacy Teradata/Oracle ETL stack to AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation)" | Cloud-native stack breadth; Iceberg signals modern data lakehouse |
| SW-3 | K8s + GitLab CI/CD | "Deployed and operated Python data apps on Kubernetes with GitLab CI/CD in agile DevOps team" | K8s + CI/CD = full DevOps ownership |
| BS-3 | Application Owner | "Application Owner for semiconductor analytics suite — SLOs, vendor management, training, documentation" | SLO ownership = senior signal |
| BS-1 | ML inference in fab | "Containerized ML inference (Docker, K8s, Ansible) into 24/7 production; automated image-based defect classification" | Production ML in constrained environment |
| BS-2 | Data services Oracle/Hadoop | "Built Python/Java/C# data services over OracleDB and Hadoop/ImpalaSQL for semiconductor analysis teams" | Multi-language, enterprise DB breadth |
| SW-4 | B2B products | "Delivered data products and dashboards for B2B stakeholders; drove process automation" | Stakeholder-facing breadth |
| BS-4 | ELK PoC | "Delivered anomaly detection PoC: ELK + Kafka, Grafana/Prometheus/Loki monitoring" | Observability initiative |

---

## S4: Skills Guide

**Bold tools (resume Technical Skills section):**
Python, Kafka, AWS (S3 · Glue · Athena · Redshift · Airflow · CloudFormation), Kubernetes, Teradata

**Must-include skills (ATS match):**
- Python, SQL, ETL/ELT
- Apache Kafka, Apache Airflow
- AWS (S3, Glue, Athena, Redshift), Apache Iceberg
- Kubernetes, Docker, GitLab CI/CD
- Teradata, Oracle DB
- PySpark

**Nice-to-have (include if JD mentions):**
- SAP BODS, Hadoop/Impala, Step Functions, Lambda
- Grafana, Prometheus, ELK Stack
- Ansible, IaC/CloudFormation
- dbt (not evidenced — do NOT claim if not in JD; omit)

**Omit:**
- RPA/UIPath, Camunda, IBM ODM (too early-career/non-core)
- HP Quality Center, Serenity-BDD, JBehave (testing tools — irrelevant)
- C++, J2EE (legacy — omit unless JD explicitly asks)

**Certifications to highlight:**
- AWS Certified Solutions Architect – Associate (active, 2024–2027) → HIGH value for this role type
- Data Engineering with AWS (Udacity, 2026) → supporting signal
- iSAQB CPSA Foundation Level → supporting (architecture awareness)

---

## S5: Cover Letter Guide

**Institution type:** Industry — tech company, scale-up, or enterprise platform team

**Opening hook pattern:**
> "As a Staff Data Engineer at Swisscom — Switzerland's largest telco — I currently own the business-critical Fulfillment ETL pipelines that feed our data warehouse, while simultaneously leading the migration of our legacy stack to a cloud-native AWS architecture. [Tie to their specific need / JD signal]."

**Key narrative thread:**
1. **Ownership at scale** — Component Owner at Swisscom, Application Owner at Bosch: not just building pipelines, but running them in production with SLA accountability
2. **Cloud-native evolution** — AWS migration (Athena/Iceberg, Glue, Airflow, CloudFormation): led the transition, not just participated
3. **Production ML integration** — Bosch: ML inference containerized into 24/7 fab; demonstrates that "data engineer who can own the ML data layer"
4. **Consistent seniority arc** — Bosch promotion (mid → Senior), Swisscom promotion (Senior → Staff)

**"Why them" angle to research:**
- What is their data stack? Match Kafka/Airflow/AWS overlaps explicitly
- Are they migrating to cloud or lakehouse architecture? → Your SW-1 experience is directly relevant
- Do they operate pipelines in production SLAs? → Component Owner + on-call duty is your signal

**Avoid:**
- Starting with "I am passionate about data"
- Listing all tools in paragraph form
- Mentioning Bundeswehr unless specifically relevant (leadership angle for management-adjacent roles)
- Overplaying test automation background