Azure Databricks Engineer - Anti-Money Laundry

Role Overview :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.Responsibilities for Internal Candidates :- Design, build, and maintain Databricks workspaces, clusters, and compute pools across development, testing, and production environments.- Configure and manage Unity Catalog for data governance, fine-grained access control, permissions, metadata management, and data lineage.- Optimize Databricks cluster configurations, including instance types, auto-scaling, spot/preemptible nodes, and compute pools to improve performance and reduce costs.- Implement workspace best practices, including folder structures, access controls, secret management using Databricks Secrets, Azure Key Vault, or AWS Secrets Manager.- Create, schedule, and manage Databricks Jobs, Workflows, and multi-task job orchestration with dependency management.- Design and implement Delta Lake tables using partitioning, Z-Ordering, OPTIMIZE, VACUUM, and file compaction techniques.- Build and maintain Medallion Architecture (Bronze, Silver, and Gold layers) for scalable and governed data lakehouse solutions.- Develop Delta Live Tables (DLT) pipelines with built-in data quality expectations for reliable ETL/ELT processing.- Manage schema evolution, table versioning, Time Travel, and Change Data Feed (CDF) to support incremental data processing.- Design and implement lakehouse architectures integrating Delta Lake with cloud storage and external systems such as Azure Data Lake Storage (ADLS), Kafka, Event Hubs, and Kinesis.- Develop scalable batch and real-time data pipelines using PySpark, Spark SQL, Structured Streaming, and Delta Lake.- Build streaming ingestion pipelines from Kafka, Azure Event Hubs, and other streaming platforms into Delta tables.- Optimize PySpark applications using broadcast joins, Adaptive Query Execution (AQE), dynamic partition pruning, caching, and Photon Engine.- Develop reusable transformation frameworks, utility libraries, and pipeline templates to improve engineering productivity and standardization.- Implement robust error handling, retry mechanisms, logging, monitoring, and dead-letter queue (DLQ) patterns for production-grade pipelines.- Set up and manage MLflow experiment tracking, model registry, and model lifecycle management.- Support machine learning workloads by enabling scalable model training, inference, and GPU-based compute environments.- Develop feature engineering pipelines using Databricks Feature Store to create reusable and versioned machine learning features.- Enable Generative AI solutions, including Retrieval-Augmented Generation (RAG), vector search, LLM fine-tuning, and Mosaic AI capabilities.- Implement MLOps best practices, including model versioning, model deployment, A/B testing, and Databricks Model Serving.- Integrate Databricks with Azure Data Lake Storage (ADLS) and other cloud-native services.- Develop and maintain CI/CD pipelines using Azure DevOps, GitHub Actions, or GitLab CI for Databricks notebooks, jobs, and workflows.- Automate Databricks infrastructure deployment using Databricks Asset Bundles (DABs), Terraform, and Infrastructure-as-Code (IaC) practices.- Build and manage data ingestion frameworks using Auto Loader, COPY INTO, and third-party integration tools such as Fivetran, dbt, and Airbyte.- Monitor pipeline execution, cluster utilization, system performance, and cloud costs using Databricks system tables and cloud monitoring tools.- Implement row-level security, column-level masking, dynamic views, and governance policies using Unity Catalog.- Enforce data quality through Delta Live Tables expectations and Great Expectations frameworks.- Perform query optimization, execution plan analysis, caching strategies, and performance tuning to improve workload efficiency.- Maintain enterprise data cataloging, metadata management, and end-to-end data lineage.- Prepare technical documentation, architecture diagrams, operational runbooks, and standard operating procedures for Databricks platform and data engineering solutions.Qualifications for Internal Candidates :- Bachelors or masters degree in computer science, Information Technology, Data Engineering, or related field.- 6+ years of total experience in data engineering or software engineering.- 3+ 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.- Deep expertise in Databricks Workspaces, Clusters, Jobs, Workflows, and Repos.- Proficiency with Unity Catalog - metastore setup .