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Mid-level generalist data role in Bangalore with common Spark/Python skills increases applicant competition.
Core data engineering skills are transferable, but industrial time-series IoT preference adds moderate domain bias.
Explicit 2–5 years requirement plus mandatory Spark, Python, and SQL skills tighten shortlisting.
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Design, build, and maintain scalable batch and streaming data pipelines focusing on time-series data from heating, refrigeration, and process equipment.
Develop and operationalize advanced analytics workflows integrating data from multiple industrial and enterprise sources to support digital product features and operational insights.
Contribute to the enhancement of the GEA Cloud Analytics Platform ensuring high data quality, reliability, and performance, while following modern software engineering practices.
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field.
2–5 years of experience in Data Engineering or related roles.
Strong experience with Spark, Databricks, or similar big data frameworks plus proficiency in Python and SQL.
Experience handling time-series data and industrial IoT or real-time analytics is highly preferred.
Experienced in developing scalable data pipelines specifically for industrial or time-series data environments.
Familiar with agile methodologies and collaborative development within cross-functional data and digital product teams.
Capable of contributing to or leading improvements in cloud-based analytics platforms using modern software engineering practices.