Job DescriptionKey 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.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.Required QualificationsEducation- Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering,



or related field.Experience- 8+ 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.Databricks Platform- 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.- Robust 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.PySpark & SQL- 4+ years of PySpark development — DataFrames, 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.Cloud Platforms- 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.- Exposure to Databricks Feature Store and Mosaic AI / GenAI capabilities.

📌 Data Engineer (Pune)
🏢 EXL
📍 Pune