Lead Data Engineer

My client is looking for a Lead Data Engineer to design and build scalable data pipelines that deliver trusted, analytics-ready datasets for BI, AI, and operational use cases across a hybrid environment.

🔧 Key Responsibilities:

• Build pipelines across bronze, silver & gold layers (Databricks, Spark, dbt)
• Implement data quality checks, contracts & schema validation
• Apply governance (catalog, lineage, RBAC, metadata)
• Deliver curated datasets, features & embeddings for AI/BI
• Monitor pipeline health, performance & cost to meet SLAs

⚙️ Tech Stack
Databricks * Spark * Delta Lake * dbt * Azure Data Factory * Kafka/Event Hubs * CI/CD (Azure DevOps/GitHub)

🔐 Governance & Ops:

• Enforce data contracts, lineage & cataloging
• Apply masking, tokenisation & access controls (PII/PHI)
• Build observable pipelines with alerts, dashboards & runbooks
• Optimize performance (partitioning, caching, cost efficiency)

✅ Requirements:

• 5+ years in Data Engineering
• Strong SQL, data modeling (dimensional/data vault)
• Proficiency in Python
• Hands-on with Databricks, Spark, Delta Lake & dbt
• Experience with Azure data services (ADF, ADLS, Key Vault)
• Familiarity with CI/CD & container basics (Docker/Kubernetes)

➕ Nice to Have:

• Streaming (Kafka/Event Hubs) & CDC (GoldenGate)
• Catalog/lineage tools (Purview, OvalEdge)
• S3-compatible storage (MinIO, VAST)
• Exposure to BI tools (Power BI) & healthcare standards (FHIR/MDR)

🎓 Education
Bachelor’s in Computer Science, Engineering, or related field

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