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Strong startup brand, generalist Data Engineer title, metro location, and broad toolset increase candidate competition.
Core data engineering skills (SQL, Python, ETL) transfer easily across industries despite financial domain context.
Explicit domain experience and specific tooling required but low years barrier reduces strictness to medium.
Design, build, and manage scalable data pipelines within Databricks data lake.
Transform raw data into clean, tested assets using SQL and tools like dbt to support AI/ML initiatives.
Collaborate with cross-functional teams to deliver actionable insights that guide products, AI models, and business decisions.
Minimum 1 year of professional data engineering experience.
Experience with data scripting/querying languages such as SQL, NoSQL, Python or R.
Hands-on with data pipeline tools like Airflow, SQL tasks, stored procedures, and AWS services including Lambda, S3, Cloudfront, SQS.
Work location requirement: In-office at Koramangala, Bengaluru.
Comfortable working at the intersection of data engineering and AI/ML support roles.
Experience in building robust data workflows that optimize performance and reliability.
Capable of balancing execution speed with quality in a fast-paced environment.