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.

What you'll do

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.

What We Offer

Modern data stack with real constraints: Kafka + NiFi + lakehouse + distributed SQL + Kubernetes.
Remote-first in Germany with regular Berlin prototyping sprints, 30 days vacation, equipment & learning budget.
High leverage: your prototypes become blueprints multiple teams reuse and productize.