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.
Skills Required
- Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field
- 6-8 years total experience in data engineering or software engineering
- 4+ years hands-on production experience with the Databricks platform
- Strong background in big data engineering, cloud data platforms, and distributed computing
- Experience developing scalable batch and streaming pipelines using PySpark and Spark SQL
- Hands-on experience with Delta Lake features: partitioning, Z-ordering, OPTIMIZE, VACUUM, schema evolution, time travel, and Change Data Feed
- Experience implementing Delta Live Tables (DLT) pipelines and Medallion (Bronze/Silver/Gold) architecture
- Experience building structured streaming ingestion from Kafka, Event Hubs, or Kinesis into Delta tables
- Experience configuring and managing Databricks workspaces, clusters, compute pools, jobs, and workflows
- Experience with Databricks Unity Catalog for data governance and fine-grained access control
- Familiarity with cloud storage integrations (ADLS, S3, GCS) and secret management (Databricks Secrets, Azure Key Vault, AWS Secrets Manager)
- Proven ability to optimize Spark jobs using techniques like broadcast joins, AQE, and dynamic partition pruning
- Experience implementing robust error handling, retry logic, and dead-letter queue patterns in production pipelines
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
