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.

* 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.

Technology->Big Data - Data Processing->Spark,Technology->Java->Apache->Scala

Join a fast-paced data engineering team where you'll build and optimize large-scale data processing solutions that power analytics and decision-making across the business. In this role, you'll use Spark and Scala to design reliable, high-performance pipelines, collaborating closely with data engineers, analysts, and platform teams to deliver clean, trusted datasets. You'll work on challenging problems like performance tuning, handling complex transformations, and ensuring data quality at scale-while contributing to a culture that values ownership, continuous improvement, and knowledge sharing. If you enjoy turning raw data into well-structured, production-ready assets and want to make a measurable impact through scalable engineering, this is a great opportunity to grow and lead through hands-on delivery.