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.

• 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

• Databricks
• Spark
• Delta Lake
• dbt
• Azure Data Factory
• Kafka/Event Hubs
• CI/CD (Azure Dev Ops/Git Hub)

• 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)

• 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)

• Streaming (Kafka/Event Hubs) & CDC (Golden Gate)
• Catalog/lineage tools (Purview, Oval Edge)
• S3‑compatible storage (MinIO, VAST)
• Exposure to BI tools (Power BI)
• Healthcare standards (FHIR/MDR)

Bachelor's in Computer Science, Engineering, or related field.