Data Engineer
Prevalent AI (company site, opens in a new tab)3–5 yrsKochiHybridDirect career pageEst. 6–14 LPA
Posted 1d ago
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Job description
Structured overview of role & requirements
About This Role
- Design, develop, test, and maintain production-grade PySpark applications using DataFrame APIs and Spark SQL for distributed data processing.
- Diagnose and resolve Spark performance bottlenecks using tools like Spark UI and History Server, applying advanced optimization techniques such as AQE and join strategies.
- Own engineering problems end-to-end from analysis through implementation, testing, deployment, and monitoring pipeline performance and reliability.
Minimum Requirements
- 3–5 years of relevant software or data engineering experience with substantial hands-on PySpark development.
- Strong programming skills in Python and PySpark with the ability to write efficient, production-quality distributed data-processing code.
- Deep understanding of Apache Spark execution internals, distributed processing, partitioning, shuffling, and performance optimization techniques.
- Strong SQL skills including joins, aggregations, window functions, and query optimization fundamentals.
Ideal Candidate Profile
- Experience focused on core Spark engineering rather than general data orchestration or pipeline tools.
- Proven ability to independently troubleshoot complex Spark performance and reliability issues using execution internals knowledge.
- Comfortable owning technical decisions and collaborating with engineers and architects to evaluate alternative solutions.