Job Description & Summary :

- Hands-on experience in PySpark development on Azure Databricks.

- End-to-end ownership of data pipeline development, from coding and version control to build, deployment, and production validation in a modern lakehouse environment.

- PySpark and Scala codebases, build Spark JARs using Maven, and execute/validate them on Azure Databricks using Spark Submit and Databricks Workflows.

Skill :

- Apache Spark on Azure Databricks

- Azure Databricks

- Pyspark

- SQL

- Scala and Hadoop.

Responsibilities :

- Design, develop, and maintain scalable batch data pipelines using Apache Spark on Azure Databricks.

- Build Spark executable JARs using Maven and manage dependencies.

- Execute and validate Spark jobs using Spark Submit on Azure Databricks with custom runtime arguments and I/O paths.

- Implement and manage Delta Lake components (Delta Tables, Delta Live Tables).

- Work with Hive Metastore and Unity Catalog for metadata, governance, and access control.

- Making code changes in Scala/PySpark projects and taking them through to production execution.

- 6+ years of overall experience in Data Engineering, with strong focus on: Apache Spark (core APIs, transformations/actions, RDD/DataFrame/Dataset APIs).

Good To Have skill sets :

- Proficiency in Pyspark and Scala for Spark development (primary language for repositories).

- Strong SQL skills for complex queries, transformations, and performance tuning.

- Solid understanding of advanced Data Engineering concepts (e.g., data modeling in lakehouse, incremental/batch patterns, CDC, SCDs, data quality frameworks).