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.

• 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.

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

• 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.