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Tier-1 brand, generalist mid-level data role with metro location and broad skills increases competition.
Core PySpark, cloud, and ETL skills are highly transferable across industries.
Explicit 3+ years and PySpark/cloud experience required, but many good-to-haves keep filters moderate.
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Develop and maintain scalable, cloud-based data pipelines and ETL/ELT workflows using PySpark and Python for large-scale data processing.
Contribute to design and implementation of modular, maintainable data engineering solutions while ensuring code quality, performance optimization, and troubleshooting.
Collaborate with cross-functional teams to deliver data solutions supporting analytics and business needs, maintaining data quality, governance, and operational excellence.
3+ years of hands-on data engineering experience with PySpark and Python.
Experience in developing and maintaining data pipelines and batch/stream processing solutions.
Familiarity with cloud platforms like AWS, Azure, or GCP and related services (e.g., S3, Glue, Databricks).
Bachelor’s degree in Computer Science, Engineering, or related field or equivalent practical experience.
Experienced in Agile/Scrum environments with ability to collaborate effectively within cross-functional teams.
Comfortable working with modern data architectures such as data lakes, lakehouse, and medallion architecture.
Skilled in applying engineering standards like version control (Git), CI/CD, and testing to ensure code quality and platform reliability.