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Strong Tier-1 brand, popular data engineer title, and metro location drive high applicant competition.
Core data engineering skills are highly transferable; finance domain knowledge is optional.
Specific AWS, Spark and data pipeline skills required but no explicit years, causing medium strictness.
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Design and develop scalable, testable data pipelines using Python and Apache Spark in a modern AWS environment.
Collaborate with product and business teams to deliver reliable, high-quality data solutions for financial indices.
Apply software engineering best practices including version control, CI/CD, automated testing, and contribute to lakehouse architecture development using Apache Iceberg.
Proficiency in Python programming with experience in writing clean, maintainable, and testable code.
Experience with data engineering fundamentals including ETL, batch processing, and schema evolution.
Familiarity with AWS data stack tools such as S3, Glue, Lambda, and EMR Serverless.
Work Experience Required: Not explicitly mentioned in the JD.
Experience or strong interest in large-scale data processing with Apache Spark and data pipeline orchestration.
Comfortable working closely with business stakeholders in Agile team environments to translate requirements into data-driven solutions.
Technical proficiency with relational databases like Aurora PostgreSQL and developing observability and data quality checks in data workflows.