* Design, develop, and maintain scalable batch/stream data pipelines using Python and PySpark in distributed environments.
* Implement efficient transformations, aggregations, and joins on large datasets while ensuring performance and cost optimization.
* Write optimized SQL for data extraction, validation, and reconciliation across multiple sources.
* Build reusable, testable modules and follow engineering best practices (code reviews, unit testing, documentation).
* Troubleshoot production issues, perform root-cause analysis, and implement long-term fixes and monitoring improvements.
* Collaborate with stakeholders to translate requirements into technical designs, delivery plans, and measurable outcomes.
* Ensure data quality through validation checks, anomaly detection patterns, and consistent schema management.
* Contribute to continuous improvement of development standards, performance benchmarks, and pipeline reliability.

* Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
* 5-9 years of hands-on experience in software development and/or data engineering roles.
* Strong proficiency in Python with experience building production-grade applications or data workflows.
* Strong proficiency in PySpark, including DataFrame APIs, optimization techniques, and distributed processing concepts.
* Working knowledge of SQL for complex queries, data analysis, and validation.
* Experience delivering reliable solutions with attention to performance, scalability, and maintainability.

Technology->Analytics - Packages->Python - Big Data,Technology->Big Data - Data Processing->PySpark

Build and scale data-driven solutions that power smarter decisions. In this role, you'll design and deliver high-performance data processing pipelines using Python and PySpark, working closely with data engineers, analysts, and product teams to turn raw data into reliable, actionable insights. You'll contribute to a collaborative environment where clean code, thoughtful design, and continuous improvement are valued. If you enjoy solving complex data challenges, optimizing distributed workloads, and delivering production-ready systems that make a real impact, this is a great opportunity to grow your expertise while helping teams move faster with trustworthy data.