Responsibilities :
Data Engineering & Development
Design, develop, and maintain scalable batch/stream data pipelines using Spark with Scala and Python.
Implement efficient transformations, joins, aggregations, and data quality checks for large datasets.
Build reusable frameworks/utilities to standardize pipeline patterns and reduce delivery time. Performance & Reliability
Tune Spark jobs (partitioning, caching, shuffles, memory/executor settings) to improve performance and cost efficiency.
Troubleshoot production issues, perform root-cause analysis, and implement preventive fixes.
Ensure reliability through robust logging, monitoring hooks, and failure-handling strategies. Collaboration & Delivery
Work with stakeholders to refine requirements and deliver well-documented, production-ready solutions.
Conduct code reviews, enforce best practices, and mentor team members on Spark/Scala/Python patterns.
Contribute to CI/CD-friendly development practices including testing, version control, and release readiness.
Additional Responsibilities:
Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
5-9 years of experience in data engineering or backend development with strong hands-on delivery ownership.
Strong proficiency in Python and Apache Spark for large-scale data processing.
Solid experience with Scala for Spark-based development and production-grade implementations.
Working knowledge of Hive and SQL-based data querying/processing concepts.
Technical and Professional Requirements:
Technology- Analytics - Packages- Python - Big Data,Technology- Big Data - Data Processing- Spark,Technology- Big Data - Data Processing- Scala
