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Tier-1 brand, metro location, and mid-level generalist data engineering role increases competition.
Core data engineering skills are transferable across industries, though financial services experience is preferred.
Explicit 7–9 years requirement plus mandatory Databricks, Spark, and Python skills increases shortlisting strictness.
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Design, develop, and optimize scalable batch ETL pipelines using Python and Apache Spark on Databricks (AWS).
Build and maintain SQL-based data transformations, data validation logic, and efficient data models to support analytics and reporting.
Ensure data quality, performance, and reliability across cloud data engineering platforms while collaborating with analysts and technology teams.
7-9 years of experience in data engineering.
Strong expertise in Python, Apache Spark, Databricks on AWS, and SQL.
Solid understanding of data warehousing and data modeling concepts.
Experience in large enterprise environments; financial services experience preferred.
Experienced in cloud-based big data engineering specifically using AWS technologies and Databricks.
Comfortable handling end-to-end batch ETL pipeline ownership and optimization in enterprise settings.
Strong grounding in data modeling and warehousing, with ability to interface effectively with analytics and tech teams.