Databricks Engineer

We are looking for a skilled and passionate 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 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. MLflow & AI/ML Workloads Set up and manage MLflow tracking servers, experiment registries, and model lifecycle management on Databricks. Support data scientists and ML engineers in deploying model training and inference workloads on Databricks clusters and GPU instances. Build feature engineering pipelines using Databricks Feature Store for reusable, versioned ML features. Enable GenAI workloads — LLM fine-tuning, RAG pipeline development, and vector search (Databricks Vector Search / Mosaic AI). Implement MLOps practices: model versioning, A/B testing, model serving via Databricks Model Serving endpoints. Cloud Integration & DevOps Integrate Databricks with cloud-native services: Azure Data Lake Storage (ADLS). Build and maintain CI/CD pipelines for Databricks notebooks and jobs using Azure DevOps, GitHub Actions, or GitLab CI. Implement Databricks Asset Bundles (DABs) or Terraform for infrastructure-as-code (IaC) deployment of Databricks resources. Manage data ingestion using Auto Loader, COPY INTO, and partner integrations (Fivetran, dbt, Airbyte). Monitor pipeline health, cluster utilization, and costs using Databricks system tables and cloud cost management tools. Governance, Security & Optimization Implement row-level security, column masking, and dynamic data views using Unity Catalog policies. Ensure data quality enforcement using Delta Live Tables expectations and Great Expectations integrations. Conduct performance tuning — query plan analysis, caching strategies, Photon engine enablement. Maintain data cataloging, metadata management, and data lineage tracking within Unity Catalog. Document architecture decisions, runbooks, and operational guides for Databricks workloads. Education Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field. Experience 4-6 years of total experience in data engineering or software engineering. 2+ 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.