Description
The role develops, tests, and maintains scalable data pipelines using Databricks, PySpark, Python, SQL, and related data-engineering technologies. Responsibilities include ingesting and transforming structured and semi-structured data, building ETL/ELT workflows, performing data quality checks, optimizing Spark jobs, troubleshooting pipeline failures, documenting processes, and supporting AI/ML data preparation. The position requires a bachelor’s degree or equivalent practical experience, 2–6 years of relevant experience, and hands-on expertise with Databricks, PySpark, Python, Delta Lake, and data engineering best practices.
