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Tier-1 brand, mid-level generalist role, metro location increase applicant competition.
Data engineering skills transferable but front-office finance experience increases domain specificity.
Explicit 5+ years, required Snowflake/Python/AWS and finance experience make filters highly strict.
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Design, build, and maintain scalable data platforms, pipelines, and services supporting quantitative research and investment workflows for front office across multiple asset classes.
Take ownership of data engineering solutions from design through testing, deployment, and first-line production support including monitoring, incident response, and troubleshooting.
Work closely with Front Office Quantitative Researchers and Data & Analytics Engineering team to ensure high-quality, reliable, and timely data delivery underpinning trading and portfolio decision-making.
Minimum 5 years professional experience in data engineering or software engineering, preferably in buy-side, sell-side or financial services.
Strong Python expertise with software engineering best practices: version control, testing, CI/CD.
Experience designing, building and supporting scalable data pipelines on cloud-native environments (preferably AWS).
Proficiency with Snowflake and NoSQL databases (particularly MongoDB).
Experienced working in fast-paced financial services environments, familiar with investment workflows and quantitative research support.
Demonstrates strong ownership mindset with proven ability to manage data engineering solutions end-to-end including production support responsibilities.
Effective at collaborating with quantitative researchers and engineering teams to translate complex business needs into technical solutions supported by strong communication skills.