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Strong Tier-1 brand, metro location, mid-level generalist data engineer title, and 3–5 years increase competition.
Core data engineering skills are broadly transferable across industries, so background sensitivity is low.
Explicit 3–5 year requirement plus mandatory PySpark, Python, SQL, ETL, and Airflow skills raise strictness.
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Design, develop, and maintain scalable ETL/ELT data pipelines using PySpark, Python, and SQL for large volumes of data.
Optimize data pipelines for performance, scalability, and reliability including Spark job tuning and error handling.
Collaborate with data teams to implement data quality checks, monitoring, and troubleshoot pipeline failures.
3–5 years of hands-on Data Engineering experience.
Proficient in PySpark/Apache Spark, Python programming, and advanced SQL skills.
Experience with data lakes, data warehouses, and cloud platforms (AWS, Azure, or GCP).
Bachelor's degree in B.Tech/B.E. or relevant field.
Experienced in building batch and streaming data pipelines using workflow orchestration tools like Apache Airflow or Databricks Workflows.
Comfortable with distributed data processing for large datasets and implementing scalable data engineering solutions.
Familiarity with Git and CI/CD practices indicating ability to work in collaborative and automated delivery environments.