Data Engineer (Python)

Responsibilities

• Prototype ingestion and connector patterns (batch + streaming) using NiFi, Kafka, Kafka Connect/Streams, and CDC approaches.
• Design "prototype-grade but adoptable" schemas and data models with clear semantics and evolution discipline.
• Build incremental lakehouse datasets (Hudi/Iceberg/Delta patterns) and produce queryable outputs for realistic latency/throughput evaluation.
• Bake in data quality and provenance mindset early (checks, metadata hooks, operability basics).
• Containerize and deploy prototypes on Kubernetes; deliver minimal runbooks/configs that make adoption straightforward.
• Produce adoption artifacts: schemas, reference implementations, technical design notes, and an integration backlog.
• About You
• 3+ years data engineering experience (level dependent) with real pipeline delivery beyond ad-hoc scripts.
• Strong Python + SQL; comfortable building transformations, validation tooling, and pipeline glue code.
• Practical streaming/CDC fundamentals (ordering, duplication, replay, idempotency) and Kafka ecosystem experience.
• Familiar with lakehouse/storage and query layers (e.g., Hudi/Iceberg/Delta, Trino/Hive/Postgres) and how to make datasets usable.
• Comfortable working in Kubernetes/container environments and documenting decisions clearly.
• Eligible to work in Germany; EU/NATO citizenship preferred and export-control screening applies.
• Nice‑to‑haves
• Great Expectations or similar data quality tooling; metadata/lineage platforms (OpenMetadata/DataHub/Atlas).
• Experience shipping in on-prem or air-gapped environments; governance/policy awareness for regulated customers.
• German language (B1+) and/or experience with OSINT/GEOINT/multi-INT data shapes.

Benefits

Additional Information

Data Engineer (Python) Company Orcrist builds the Orcrist Intelligence Platform (OIP), a Kubernetes-based data intelligence system delivered as SaaS or self-hosted/on-prem (including air-gapped deployments). We run streaming and batch pipelines that power search, ML enrichment, and investigative workflows for mission-critical customers. Role Rapidly validate new data initiatives end-to-end-without sacrificing adoptability. On Innovation, you'll prototype representative connectors and pipelines (batch + streaming), generate credible performance/operability readouts, and ship a handoff package that Foundation or a delivery team can productize.

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