AWS Data Engineer

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

Nice-to-haves:

• It's great (but not required) if you also bring:
• Experience with Apache Iceberg or similar table formats
• Familiarity with CI/CD tools like GitLab CI, Jenkins, or GitHub Actions
• Exposure to data quality frameworks like Great Expectations or Deequ
• Curiosity about financial markets, index data, or investment analytics

Note: Hybrid (2 or 3 days a week, Onsite)