Senior Databricks Engineer

We are looking for a skilled and passionate Senior Databricks Engineer to design, build, and optimize enterprise-scale data lakehouse solutions on the Databricks platform. The successful candidate will be responsible for creating Databricks pipeline delivering Financial Crime platforms covering Anti-Money Laundering (AML), Know Your Customer (KYC), Customer Risk Assessment (CRA), Sanctions Screening, Transaction Monitoring, Fraud Detection, and Regulatory Reporting

Education

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field.

Experience

• 6-8 years of total experience in data engineering or software engineering.
• 4+ years of dedicated hands-on experience with the Databricks platform in production environments.
• Strong background in big data engineering, cloud data platforms, and distributed computing.

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.
• Write optimized PySpark transformations leveraging broadcast joins, adaptive query execution (AQE), and dynamic partition pruning.
• Create reusable transformation libraries, utility frameworks, and pipeline templates for team productivity.
• Implement robust error handling, retry logic, and dead-letter queue patterns in production pipelines.