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Senior Databricks Engineer

exl Service.com (India) Private Limited
Pune, IND
Senior
Hybrid
Discovered 1 months ago

Full Job Posting

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.

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