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

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:

Ownership at scale — Component Owner at Swisscom, Application Owner at Bosch: not just building pipelines, but running them in production with SLA accountability
Cloud-native evolution — AWS migration (Athena/Iceberg, Glue, Airflow, CloudFormation): led the transition, not just participated
Production ML integration — Bosch: ML inference containerized into 24/7 fab; demonstrates that "data engineer who can own the ML data layer"
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