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Tier-1 brand, common data-engineer title, metro location, and broad AWS/Spark requirements increase applicant competition.
Financial indices domain increases domain bias, though AWS, Spark, and Python skills are transferable across industries.
Explicit 10–15 years and mandated AWS Spark data-engineering experience make shortlisting highly strict.
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Design and develop scalable, testable data pipelines using Python and Apache Spark in a modern AWS environment.
Orchestrate data workflows with AWS tools (Glue, EMR Serverless, Lambda, S3) and contribute to building a lakehouse architecture using Apache Iceberg.
Collaborate closely with product and business teams to translate requirements into reliable, high-quality data solutions with observability and quality checks.
10 to 15 years of professional experience in data engineering or related fields.
Proficient in Python programming with clean, maintainable code practices including tests (e.g., pytest).
Experience with AWS data stack components such as S3, Glue, Lambda, and EMR or strong willingness to learn.
Familiarity with batch processing, schema evolution, and building ETL/data pipelines.
Experienced in large-scale data processing using Apache Spark and preferably familiar with Apache Iceberg or similar table formats.
Comfortable working in Agile teams with collaboration focus, engaging with business stakeholders to understand requirements.
Practitioner of modern software engineering practices like CI/CD, automated testing, code reviews, and modular design within data engineering contexts.