Data Engineer II (Databricks & Pyspark)

Build and maintain scalable data pipelines and lakehouse solutions on Databricks, ensuring clean, reliable data delivery to analytics and ML teams.

Design and implement ELT pipelines using PySpark and Databricks Workflows while optimizing data storage and retrieval for performance and cost.

Orchestrate complex workflows with Azure Data Factory, monitor pipeline reliability, and collaborate with analysts and data scientists to deliver trusted datasets.

Minimum 4+ years experience in data engineering with at least 3+ years working with Databricks.

Strong skills in PySpark (DataFrames, Spark SQL, window functions, UDFs) and advanced SQL (complex joins, CTEs, aggregations, query optimization).

Experience with Delta Lake features including ACID transactions, time travel, and schema evolution; familiarity with Unity Catalog and data governance.

Cloud experience required with Azure (ADLS Gen2, ADF) or equivalent AWS/GCP services; proficiency with Git, automated testing, and notebooks-as-code.

Experienced data engineer with a focus on Databricks ecosystem and PySpark for large scale data pipeline development.

Comfortable working in cloud environments (especially Azure) with orchestration tools and maintaining high availability data workflows.

Proficient in collaborating with cross-functional analytics and data science teams to deliver high-quality, trusted datasets.