About the role
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
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
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)