Databricks Engineer

Responsibilities

• Databricks Platform Engineering
• Design, build, and maintain Databricks workspaces, clusters, and compute pools across dev/test/prod environments.
• Configure and manage Databricks Unity Catalog for data governance, access control, fine-grained permissions, and data lineage.
• Optimize cluster configurations — instance types, auto-scaling policies, spot/preemptible nodes — for cost and performance.
• Implement workspace-level best practices: folder structures, access controls, secret management (Databricks Secrets / Azure Key Vault / AWS Secrets Manager).
• Manage Databricks jobs, workflows, and multi-task job orchestration with dependency management.
• Delta Lake & Lakehouse Architecture
• Design and implement Delta Lake tables with appropriate partitioning, Z-ordering, and file compaction (OPTIMIZE / VACUUM).
• Build Medallion Architecture (Bronze / Silver / Gold) layers for structured data lake organization.
• Implement Delta Live Tables (DLT) pipelines for declarative, reliable ETL/ELT with built-in data quality expectations.
• Manage schema evolution, table versioning, time travel, and Change Data Feed (CDF) for incremental processing.
• Design data lakehouse patterns integrating Delta Lake with external systems (Kafka, ADLS, S3, GCS).
• Data Pipeline Development (PySpark / SQL)
• Develop scalable batch and streaming data pipelines using PySpark, Spark SQL, and Delta Lake.
• Build structured streaming pipelines for real-time ingestion from Kafka, Event Hubs, and Kinesis into Delta tables.