Data Engineer – Python, PySpark, SQL & ETL
Synechron (company site, opens in a new tab)5+ yrsBengaluruOn-siteDirect career pageEst. 12–20 LPA
Posted 11h ago
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Job description
Structured overview of role & requirements
About This Role
- Build, maintain, and optimize Python/PySpark ETL pipelines, data marts, and distributed data-processing solutions.
- Own delivery across development, testing, UAT support, deployment, production monitoring, and issue resolution.
- Improve data quality, query and pipeline performance, and reliability through validation, tuning, and production support.
Minimum Requirements
- Work Experience Required: Minimum 5 years in data engineering, ETL, data warehousing, big-data processing, or a related technical role.
- Hands-on experience with Python, PySpark/Spark, Pandas, SQL and Oracle SQL, ETL, data marts, data warehousing, Hadoop, Hive, MapReduce, and SQL/NoSQL databases.
- Experience with end-to-end SDLC, including testing, UAT support, production deployment, data validation, and post-production support; Jupyter Notebook and CI/CD experience are also required.
- Bachelor’s degree in Computer Science, IT, Engineering, Data Science, or a related field; equivalent relevant experience may be considered.
Ideal Candidate Profile
- Has production experience building and supporting large-scale data pipelines and data marts, beyond academic or prototype work.
- Can contribute across the delivery lifecycle, including debugging data issues, validating transformations, supporting releases, and resolving production incidents.
- Banking or financial-services experience is preferred; enterprise-scale, high-volume, regulated, or business-critical data experience is also a relevant fit signal.