Key Responsibilities

- Design, build, and optimize end-to-end ETL/ELT pipelines in Databricks using Delta Lake, Delta Live Tables (DLT), Auto Loader, PySpark, and Spark SQL for high-volume, multi-format partner ingestion.

- Implement Medallion (zoned) architecture – Raw (bronze), Standardized (silver) with advanced validation, quarantine/reject logic, schema enforcement, and Curated (gold) consumer-ready datasets optimized for downstream COB/PI analytics.

- Leverage Unity Catalog for data governance, access control, lineage, and secure multi-tenant data management.

- Develop incremental processing, change data capture (CDC), backfill strategies, late-arriving data handling, and partitioning/optimization techniques (Z-Ordering, Liquid Clustering, Auto-Optimize) to eliminate performance bottlenecks.

- Build robust data quality frameworks using Delta constraints, expectations, and monitoring to ensure clean, reliable data for downstream consumption.

- Create production-grade Databricks Workflows, Jobs, and orchestration for reliable batch and near-real-time processing using Spark Structured Streaming.

- Perform data profiling, mapping, reconciliation, and performance tuning of large-scale Spark jobs on Databricks clusters.

- Collaborate with Senior Data Architect and Data Modeller to translate target-state lakehouse design into implementable, testable increments.

- Deliver shippable, production-ready increments in Agile sprints within the implementation window, including CI/CD integration, unit/integration testing, and operational runbooks.





- Establish comprehensive observability using Databricks Lakehouse Monitoring, SQL Alerts, and dashboards for pipeline health and SLA compliance.

Requirements

Required Qualifications & Experience

- 8+ years of hands-on data engineering experience

- 5+ years building enterprise-scale solutions on Databricks (Unity Catalog, Delta Lake, Delta Live Tables)

- Proven track record delivering Medallion/zonal lakehouse architectures in production

- Solid experience with high-volume, regulated data workloads (claims, financial, or healthcare data highly preferred)

Technical Skills – Databricks Expertise (Core)

- Databricks Platform: Unity Catalog, Delta Lake, Delta Live Tables (DLT), Auto Loader, Workflows, Jobs, Repos, Lakehouse Monitoring

- Core Technologies: PySpark, Spark SQL, Spark Structured Streaming, Delta constraints & expectations

- Optimization & Performance: Liquid Clustering, Z-Ordering, Auto-Optimize, Dynamic Partition Overwrite, Photon engine

- Governance & Quality: Unity Catalog ACLs, data lineage, schema evolution, Great Expectations (or equivalent)

- Orchestration & CI/CD: Databricks Workflows, dbt on Databricks, Git integration, Azure DevOps / Jenkins

- Languages: Expert Python (PySpark), SQL

- Cloud: AWS/Azure/GCP (Databricks on any cloud)

Preferred Qualifications

- Prior experience modernizing healthcare claims data lakes (COB, Payment Integrity, Medicaid/Medicare)

- Exposure to partner ingestion patterns, multi-format data (EDI, flat files, APIs), and downstream analytical workloads

- Familiarity with CMS/HIPAA data handling and compliance in Databricks environments

📌 Cloudera Data Engineer (Bengaluru)
🏢 McLaren Strategic Solutions
📍 Bengaluru