Lead Databricks Engineer

Role & responsibilities

We are looking for an experienced Azure Databricks Engineer with strong hands-on expertise in Python, SQL, and Apache Spark to design, build, and optimize scalable data pipelines and analytics solutions on the Azure cloud platform. The ideal candidate should have experience working with large datasets, distributed data processing, and modern data engineering practices.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Azure Databricks
• Implement ETL/ELT workflows using PySpark, Spark SQL, and Python
• Optimize Spark jobs for performance, cost, and scalability
• Work with structured and semi-structured data (Parquet, Delta, JSON, CSV)
• Build and manage Delta Lake tables (ACID, time travel, schema evolution)
• Integrate Databricks with Azure Data Lake Storage (ADLS Gen2)
• Develop complex queries and transformations using SQL
• Collaborate with data scientists, analysts, and stakeholders to support analytics and ML use cases
• Ensure data quality, validation, and monitoring
• Follow best practices for security, access control, and governance in Azure

Preferred candidate profile

• 6+ years of experience in Data Engineering
• Strong hands-on experience with Azure Databricks
• Proficiency in Python for data processing
• Strong knowledge of SQL (joins, window functions, performance tuning)
• Hands-on experience with Apache Spark / PySpark
• Experience working with Delta Lake
• Knowledge of Azure Data Lake Storage (ADLS Gen2)
• Understanding of distributed computing concepts
• Experience with Git version control

Additional information

• Experience with Azure Data Factory
• Exposure to CI/CD pipelines (Azure DevOps, GitHub Actions)
• Basic understanding of data modeling
• Familiarity with cloud security and RBAC in Azure
• Exposure to streaming data (Spark Structured Streaming, Event Hub, Kafka)