Databricks Tech Lead
At a glance
Senior Data Engineering role at Fa Ewjt Saasfaprod1. Bengaluru · full-time.
Pay not stated Data Engineering salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain Databricks workspaces and compute resources across development, test and production environments
- Organize Delta Lake data with medallion layers, table optimization and Delta Live Tables pipelines
- Develop PySpark and SQL batch or streaming pipelines that ingest data from Kafka, Event Hubs and Kinesis
- Tune cluster and query performance, and apply Unity Catalog controls for access, masking and lineage
- Operate MLflow tools and feature pipelines, and help data scientists deploy training and inference workloads
What you bring
- You can build scalable batch and streaming data workflows using PySpark, Spark SQL and Delta Lake
- You have hands-on experience managing Databricks workspaces, Unity Catalog, jobs and compute resources
- You can implement Delta Lake structures, including medallion layers, table optimization and schema changes
Who this fits
This senior role suits someone focused on Databricks platform engineering, data pipelines, governance and ML workloads. It is based in Bengaluru, India, with a hybrid work arrangement.
From the employer
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). EXL Service 10 Exchange Place, Suite 2200, Jersey City, NJ T: +1.201.748.4700 www.exlservice.com 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.
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). EXL Service 10 Exchange Place, Suite 2200, Jersey City, NJ T: +1.201.748.4700 www.exlservice.com 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.
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). EXL Service 10 Exchange Place, Suite 2200, Jersey City, NJ T: +1.201.748.4700 www.exlservice.com 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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