Cloudera Data Engineer

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
  
• Strong 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