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Metro locations, mid-level generalist title, and broad cloud/data stack increase competition.
Data engineering skills are broadly transferable across industries; fintech domain adds moderate bias.
Explicit 3–7 years requirement plus specific cloud, ETL, and data warehouse skills required.
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Design, build, and maintain scalable batch and streaming data pipelines with strong performance and fault tolerance.
Develop and optimize ETL processes and manage databases/data warehouses on platforms like Snowflake and PostgreSQL.
Ensure data quality, integrity, and security while collaborating with analysts, engineers, and stakeholders to deliver reliable data solutions.
3-7 years of experience in Data Engineering.
Strong SQL skills and experience with relational databases such as Snowflake, PostgreSQL, or MySQL.
Proficiency in Python development including knowledge of AWS SDKs like Boto3.
Experience with cloud data services and ETL tools such as AWS Glue, AWS Lambda, AWS EMR, Talend, or Azure Data Factory.
Experienced in building and managing scalable data pipelines in cloud environments, preferably AWS.
Comfortable working closely with cross-functional teams in a dynamic, agile environment to integrate data solutions.
Familiarity with data warehousing concepts and modern data architecture, with preference for candidates from FinTech or payments industry though not mandatory.