Mandatory Skills: Python, PySpark, AWS, Cloud, AWS Services, AWS Components Designing and developing scalable, testable data pipelines using Python and Apache Spark Orchestrating data workflows with AWS tools like Glue, EMR Serverless, Lambda, and S3 Applying modern software engineering practices: version control, CI/CD, modular design, and automated testing Contributing to the development of a lakehouse architecture using Apache Iceberg Collaborating with business teams to translate requirements into data-driven solutions Building observability into data flows and implementing basic quality checks Participating in code reviews, pair programming, and architecture discussions Continuously learning about the financial indices domain and sharing insights with the team WHAT YOU'LL BRING: Writes clean, maintainable Python code (ideally with type hints, linters, and tests like pytest) Understands data engineering basics: batch processing, schema evolution, and building ETL pipelines Has experience with or is eager to learn Apache Spark for large-scale data processing Is familiar with the AWS data stack (eg S3, Glue, Lambda, EMR) Enjoys learning the business context and working closely with stakeholders Works well in Agile teams and values collaboration over solo heroics ...