Databricks tech lead

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.