Databricks Engineer

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

• Bachelor's or master's 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, catalog/schema/table management, access controls, and data lineage.
• Hands-on experience with Delta Live Tables (DLT) — pipeline development, expectations, and monitoring.
• Strong command of Delta Lake internals — transaction log, ACID guarantees, file layout, and optimization techniques.
• Experience with Databricks SQL Warehouses, SQL Analytics, and dashboard creation.
• Knowledge of Databricks Photon engine, serverless compute, and cost optimization strategies.
• 4+ years of PySpark development — Dataframe, Datasets, Spark SQL, RDD operations.
• Expert-level SQL — window functions, lateral joins, CTEs, recursive queries, and analytical functions.
• Experience with Spark performance tuning — AQE, query plans (EXPLAIN), partitioning, and caching.
• Proficiency with Python for pipeline development, utilities, and automation.
• Hands-on experience with at least one: Azure (ADLS Gen2, ADF, Azure Databricks), AWS (S3, EMR, Glue, AWS Databricks), or GCP (GCS, BigQuery, Dataproc).
• Experience with cloud networking for Databricks: VNet/VPC injection, private endpoints, and firewall configurations.
• Familiarity with IAM roles, managed identities, and service principal authentication for Databricks.

MLflow & ML Engineering (Nice to Have):

• Working knowledge of MLflow — experiment tracking, model registry, and deployment.
• Experience supporting ML pipelines on Databricks for training, evaluation, and serving.