Responsibilities :
Data Engineering & Development
Design, develop, and maintain Spark-based batch processing pipelines using Scala for large datasets.
Implement efficient transformations, aggregations, and joins, ensuring correctness and scalability.
Write optimized SQL for data extraction, validation, and reconciliation across sources and targets. Performance, Quality & Reliability
Tune Spark jobs (partitioning, caching, shuffles, memory/executor settings) to improve runtime and cost efficiency.
Build data quality checks and validations to ensure accuracy, completeness, and consistency of outputs.
Troubleshoot production issues, perform root-cause analysis, and implement preventive fixes. Collaboration & Delivery
Work with stakeholders to understand data requirements and translate them into technical solutions.
Participate in code reviews, follow engineering best practices, and contribute to reusable components.
Document pipelines, logic, and operational runbooks for maintainability and onboarding.
Additional Responsibilities:
Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
5-9 years of overall experience with strong hands-on development in Spark and Scala.
Solid experience writing and optimizing SQL for analytics and data processing use cases.
Strong understanding of distributed processing concepts, data transformations, and performance considerations.
Ability to debug and resolve issues in data pipelines with a focus on reliability and quality.