Senior Data Engineer / ML Platform Engineer (Azure Databricks & GenAI)
Summary
We are augmenting an internal team with contract contractors who have strong Azure Databricks and ML data platform skills. Our staff already owns platform standards, Unity Catalog
governance, and architecture; we need additional capacity to deliver lakehouse pipelines, catalogued engineering and enterprise datasets, and GenAI-ready data products (embeddings,
retrieval, batch enrichment) in parallel with other priorities.
Assignments may span multiple programs—not a single fixed product—so candidates should be comfortable ramping on new domains and codebases quickly.
Responsibilities
● Design and build production data pipelines on Databricks (PySpark/SQL, Delta Lake, Unity Catalog, Workflows/Jobs) with idempotency, testing, monitoring, and clear lineage.
● Model bronze / silver / gold layers and data contracts (schemas, SLAs, quality checks) for analytics and downstream applications.
● Develop ML-oriented data products: feature stores or curated tables, embedding generation, chunking/indexing for RAG, batch classification/enrichment, and evaluation
of retrieval/data quality.
● Integrate with Azure services (ADLS Gen2, Key Vault, managed identities, private connectivity) following enterprise security and compliance patterns.
● Partner with application and ML teams to expose stable APIs, tables, and volumes that power agentic workflows (tool-backed assistants, multi-step orchestration) without
owning full UI or real-time serving unless scoped.
● Document data dictionaries, runbooks, and handoff materials; participate in code review and agile ceremonies with full-time staff.
Required qualifications
● 5+ years in data engineering, including 2+ years production Databricks on Azure.
● Strong Python (PySpark, pandas); SQL; experience operationalizing jobs at scale.
● Hands-on Delta Lake and Unity Catalog (schemas, grants, environments, promotion patterns).
● Experience building pipelines that feed ML/GenAI use cases: embeddings, vector or hybrid search inputs, labeled datasets, batch inference outputs.
● Git, CI-friendly repo layout (notebooks + packages), unit/integration testing for data pipelines.
● Strong communication; effective in staff augmentation models (embedded with internal leads, shared standards).
Preferred qualifications
● GenAI / agentic systems: RAG design, retrieval evaluation, metadata for grounding, guardrails-aware data prep.
● Multi-source integration: documents, PLM/BOM-style structured data, files in object storage, APIs, spreadsheets—normalized into a catalog.
● Exposure to containerized or microservice backends (for collaboration with app teams, not primary ownership).
Nice to have (role-dependent)
● MLOps on Databricks (Model Serving, Feature Serving, experiment tracking).
● Stream or near-real-time ingestion (Structured Streaming, event-driven triggers).
● Infrastructure-as-code literacy (Terraform/Bicep)—often owned internally; ability to consume outputs is enough.
● Graph or highly relational domains (requirements traceability, product structure, hierarchies).
● Automotive or complex manufacturing data (helpful, not required).
What we provide
● Platform expertise, Databricks/Azure standards, architecture review, and access to tooling and repos per assignment.
● Clear priorities from product/engineering owners; expectations to align with existing team patterns rather than invent a separate stack.
