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Senior Data Engineer ML Platform Engineer (Azure Databricks & GenAI)
Job Location:
Job Location:
Warren, OH - USA
Monthly Salary:
Not provided by the employer
Monthly Salary:
Posted:
17 October 2026 (7 hours ago)
Posted:
Application Deadline:
14 January 2027
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Tekfortune is a fast-growing consulting firm specialized in permanent contract & project-based staffing services for worlds leading organizations in a broad range of this quickly changing economic landscape virtual recruiting and remote work are critical for the future of work. To support the active project demands and skills gaps our staffing experts can help you find the best job for you.
Senior Data Engineer / ML Platform Engineer (Azure Databricks & GenAI)
Location: Onsite Warren MI or Austin TX 4 days per week
6 Months Contract to Hire
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 programsnot a single fixed productso candidates should be comfortable ramping on new domains and codebases quickly.
Responsibilities
For more information and other jobs available please contact our recruitment team at . To view all the jobs available in the USA and Asia please visit our website at .
Senior Data Engineer / ML Platform Engineer (Azure Databricks & GenAI)
Location: Onsite Warren MI or Austin TX 4 days per week
6 Months Contract to Hire
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 programsnot a single fixed productso 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.
- I ntegrate 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 p ower agentic workflows (tool-backed assistants multi-step orchestration) without owning full UI or real-time serving unless scoped.
- D ocument data dictionaries runbooks and handoff materials; participate in code review and agile ceremonies with full-time staff.
- 5 years in data engineering including 2 years production Databricks on Azure.
- S trong Python (PySpark pandas); SQL; experience operationalizing jobs at scale.
- Hands-on Delta Lake and Unity Catalog (schemas grants environments promotion patterns).
- E xperience 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 l eads shared standards).
- 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 spreadsheetsnormalized into a catalog.
- Exposure to containerized or microservice backends (for collaboration with app teams not primary ownership).
- 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).
- 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.
For more information and other jobs available please contact our recruitment team at . To view all the jobs available in the USA and Asia please visit our website at .
Required Experience:
Senior IC
