10+ years of experience in Data Engineering / Big Data / Analytics.
4+ years of hands-on Databricks experience preferred.
Strong experience designing enterprise-scale data platforms.
Demonstrated experience leading technical projects and mentoring engineers.
Strong communication and stakeholder-management skills.
Design and develop scalable data engineering solutions using Databricks and Lakehouse architecture.
Build robust ETL/ELT pipelines using PySpark, Spark SQL, Python, and SQL.
Design and implement Bronze, Silver, and Gold/Medallion architecture.
Develop and optimize Delta Lake tables, including MERGE, schema evolution, Change Data Feed, and incremental processing.
Build batch and real-time/streaming pipelines using Structured Streaming, Auto Loader, and Lakeflow.
Develop and manage Databricks Jobs/Workflows for pipeline orchestration, scheduling, dependencies, retries, and monitoring.
Implement enterprise data governance using Unity Catalog, including access control, data lineage, auditing, catalogs, schemas, and external locations. Unity Catalog provides centralized governance, access control, lineage, and auditing across Databricks data and AI assets. ()
Perform Spark and Databricks performance tuning, including cluster configuration, partitioning, caching, query optimization, Photon, and workload optimization.
Design data models supporting Data Warehousing, BI, Analytics, and AI/ML workloads.
Integrate Databricks with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Work with cloud services such as AWS S3, Azure ADLS Gen2, Azure Data Factory, AWS Glue, Synapse, Event Hubs/Kafka/Kinesis, as applicable.
Implement CI/CD pipelines using Git, Azure DevOps/GitHub/Jenkins and Databricks deployment capabilities.
Work with Terraform/IaC for infrastructure provisioning and automation.
Troubleshoot production pipeline failures, performance issues, data-quality problems, and Spark/cluster issues.
Establish data quality, monitoring, logging, and observability practices.
Provide technical leadership, code reviews, architecture guidance, and mentorship to junior/mid-level engineers.
Collaborate with Data Architects, Data Scientists, Business Analysts, DevOps teams, and application teams.
MLflow is particularly useful if the role touches ML/AI, as Databricks supports model tracking, lifecycle management, and deployment workflows alongside governed data. ()
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