Principal Analytics Engineer (f/m/d)
HybridBerlin, State of Berlin, Germany or Köln, North Rhine-Westphalia, Germany
Job Summary
Define and evolve the lakehouse architecture (S3 + Apache Iceberg + Athena + dbt) with teams, establishing table designs, partitioning, and schema evolution standards. Build genuine data products in the Product domain that set patterns for others, ensuring reliable consumption by humans, APIs, and AI agents. Design metadata and semantic layers to support AI-ready querying, while tracking the analytics landscape to drive adoption of proven approaches. Mentor analysts and engineers to raise review quality and technical clarity, constructively challenging the status quo. This hands-on technical leadership role offers autonomy within the Data Service Domain, where you shape how the organization builds its shared foundation for self-service reporting and AI access.
Required Qualifications
- Deep, hands-on lakehouse experience
- Designed and built lakehouse architectures
- Strong with open table formats (Apache Iceberg or equivalent)
- Strong with object storage (S3)
- Strong with serverless query engines (Athena or equivalent)
- Strong with dbt
- Excellent analytics-engineering craft
- Data-product and dimensional modeling
- Testing
- CI/CD
- Real instinct for query performance and cost
- Demonstrated technical leadership beyond a single team
- Set standards or architecture that other teams adopted
- Can influence without formal authority
- A product mindset
- Treat data as a product with consumers and SLAs
- Including non-human consumers (APIs, AI agents)
- Concrete, hands-on AI experience in your own work
- Genuine curiosity and a bias toward responsible experimentation
- Comfort with autonomy and ambiguity
- Set direction
- Make calls
- Bring people with you
- Cologne (hybrid) or Berlin (remote)
- Permanent Contract (Full-time)
Desired Qualifications
- Define and evolve the lakehouse architecture (S3 + Apache Iceberg + Athena + dbt): table and layer design, partitioning and compaction, schema evolution, multi-team isolation, and unified catalog & discoverability
- Design together with the teams, not in isolation
- Modeling conventions
- The semantic layer
- Data-product definitions
- Data-quality
- Documentation and metadata
- Build data products in the Product domain that set the pattern others follow
- Ship, not just advise
- Design metadata and semantic layers so agents query our metrics correctly
- Pioneer concrete AI use cases in the build workflow (AI-assisted modeling, testing, documentation, review)
- Track the lakehouse, analytics-engineering, and AI landscape
- Run lightweight evaluations
- Drive adoption of approaches that earn their place
- Mentor analysts and engineers
- Raise review quality
- Create technical clarity
- Constructively question the status quo
- Direct support from your Engineering Manager, ensuring your growth and success
- Participate in monthly health activities and earn an extra vacation day for achieving health goals
- Enjoy your 'Self Education Day' every last Friday of the month, where you can focus on personal growth, attend internal tech talks, read a book, or work on your goals
- Flexible working options and a focus on work-life balance
- No late-night work expected
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