Design, build, and optimize Databricks-based lakehouse solutions for financial crime use cases. Manage Databricks workspaces, clusters, Unity Catalog, and jobs. Develop scalable batch and streaming PySpark/Spark SQL pipelines (Kafka/Event Hubs/Kinesis) using Delta Lake, DLT, and Medallion architecture. Ensure data governance, performance tuning, error handling, and integration with cloud storage (ADLS/S3/GCS) and secret management.
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
ResponsibilitiesDatabricks 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.
Qualifications
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.
EXL Chennai, Tamil Nadu, IND Office
Chennai, India
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