Data Engineer

Data Engineer (Lakehouse & Hybrid Data Platforms)

My client is looking for a 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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