Orcrist Technologies
Technology
DataEngineer(Python)
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Engineer (Python) at Orcrist Technologies. Skills: Data Engineering, Python, Streaming, Kubernetes. Prototype ingestion patterns. Prototype connector patterns”
What You'll Achieve.
Ship handoff package; Produce credible readouts; Make datasets usable
Industry & Context.
Troubleshooting; Root cause analysis
Export-control screening
What They're Looking For.
Must Have
3+ years data engineering experience, Python experience, Practical streaming/CDC fundamentals, Kafka ecosystem experience, Kubernetes/container environments experience
Nice to Have
EU/NATO citizenship preferred, Great Expectations experience, OpenMetadata experience, DataHub experience, Atlas experience, On-prem experience, Air-gapped governance experience, German language (B1+), OSINT data shapes experience, GEOINT data shapes experience, Multi-INT data shapes experience
What You'll Do.
Prototype ingestion patterns
Prototype connector patterns
Build lakehouse datasets
Produce queryable outputs
Bake in data provenance
Containerize prototypes
Produce adoption artifacts
How You'll Work.
Team & Collaboration
Foundation teams; Delivery teams
Communication Scope
Technical design notes; Runbooks; Reference implementations
Full Job Description
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-co
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