123 lines
6.6 KiB
Markdown
123 lines
6.6 KiB
Markdown
# Bundle: Staff / Senior Data Engineer
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> Target employers: Tech companies, scale-ups, platform teams
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> Tier: 1 — strongest evidence, full portfolio
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> Config key: bundle_data_engineer.md
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---
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## S1: Role Profile & Priority Matrix
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**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.
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**Promotion signal to use:** "Promoted from Senior to Staff Engineer (Engineer IV) at Swisscom, April 2025."
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### Priority Matrix
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| Priority | Achievement IDs | Rationale |
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|----------|----------------|-----------|
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| 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 |
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| 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 |
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| LOW | FC-2, FC-3, VZ-1, GN-2, GN-3, CA-1 | Earlier career / non-core for this audience |
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**2-page resume bullet allocation (typical):**
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- Swisscom: 3–4 bullets (SW-1, SW-2, SW-3, SW-4 or SW-5)
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- Bosch: 3 bullets (BS-1, BS-2, BS-3; +BS-4 if space)
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- Fraunhofer: 1–2 bullets (FC-1 compressed)
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- Vizrt: 1 bullet (VZ-1 + VZ-2 combined)
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- Generali: 1 bullet (GN-1)
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---
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## S2: Summary Guide
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**Headline pattern:**
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> "Staff Data Engineer | AWS · Kafka · Kubernetes | ETL Pipelines, Cloud Migration & Production ML"
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**Building blocks** (3–5 phrases that should appear in summaries for this role type):
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- "end-to-end ETL pipeline ownership" or "component ownership of business-critical data pipelines"
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- "cloud migration" or "legacy-to-AWS migration"
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- "Kafka-based event-driven ingestion"
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- "Kubernetes deployment" or "containerized data applications"
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- "AWS Certified Solutions Architect" (cert signal)
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**Tone:** Engineer who owns systems, not just builds them. Accountability + delivery. Operator mindset.
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**Avoid:**
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- Academic or research framing
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- "Passionate about data" clichés
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- Overemphasizing testing/QA background (earlier career)
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- Listing every tool — focus on the stack that matters for the JD
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---
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## S3: Achievement Reframing Map
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| ID | Default Framing | This Role's Framing | Key Metric / Signal |
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|----|----------------|--------------------|--------------------|
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| 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 |
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| 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 |
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| 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 |
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| BS-3 | Application Owner | "Application Owner for semiconductor analytics suite — SLOs, vendor management, training, documentation" | SLO ownership = senior signal |
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| 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 |
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| 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 |
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| SW-4 | B2B products | "Delivered data products and dashboards for B2B stakeholders; drove process automation" | Stakeholder-facing breadth |
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| BS-4 | ELK PoC | "Delivered anomaly detection PoC: ELK + Kafka, Grafana/Prometheus/Loki monitoring" | Observability initiative |
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---
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## S4: Skills Guide
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**Bold tools (resume Technical Skills section):**
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Python, Kafka, AWS (S3 · Glue · Athena · Redshift · Airflow · CloudFormation), Kubernetes, Teradata
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**Must-include skills (ATS match):**
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- Python, SQL, ETL/ELT
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- Apache Kafka, Apache Airflow
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- AWS (S3, Glue, Athena, Redshift), Apache Iceberg
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- Kubernetes, Docker, GitLab CI/CD
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- Teradata, Oracle DB
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- PySpark
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**Nice-to-have (include if JD mentions):**
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- SAP BODS, Hadoop/Impala, Step Functions, Lambda
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- Grafana, Prometheus, ELK Stack
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- Ansible, IaC/CloudFormation
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- dbt (not evidenced — do NOT claim if not in JD; omit)
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**Omit:**
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- RPA/UIPath, Camunda, IBM ODM (too early-career/non-core)
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- HP Quality Center, Serenity-BDD, JBehave (testing tools — irrelevant)
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- C++, J2EE (legacy — omit unless JD explicitly asks)
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**Certifications to highlight:**
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- AWS Certified Solutions Architect – Associate (active, 2024–2027) → HIGH value for this role type
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- Data Engineering with AWS (Udacity, 2026) → supporting signal
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- iSAQB CPSA Foundation Level → supporting (architecture awareness)
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---
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## S5: Cover Letter Guide
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**Institution type:** Industry — tech company, scale-up, or enterprise platform team
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**Opening hook pattern:**
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> "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]."
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**Key narrative thread:**
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1. **Ownership at scale** — Component Owner at Swisscom, Application Owner at Bosch: not just building pipelines, but running them in production with SLA accountability
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2. **Cloud-native evolution** — AWS migration (Athena/Iceberg, Glue, Airflow, CloudFormation): led the transition, not just participated
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3. **Production ML integration** — Bosch: ML inference containerized into 24/7 fab; demonstrates that "data engineer who can own the ML data layer"
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4. **Consistent seniority arc** — Bosch promotion (mid → Senior), Swisscom promotion (Senior → Staff)
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**"Why them" angle to research:**
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- What is their data stack? Match Kafka/Airflow/AWS overlaps explicitly
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- Are they migrating to cloud or lakehouse architecture? → Your SW-1 experience is directly relevant
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- Do they operate pipelines in production SLAs? → Component Owner + on-call duty is your signal
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**Avoid:**
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- Starting with "I am passionate about data"
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- Listing all tools in paragraph form
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- Mentioning Bundeswehr unless specifically relevant (leadership angle for management-adjacent roles)
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- Overplaying test automation background
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