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 Salt is acting as an Employment Agency in relation to this vacancy.