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Tier-1 brand plus mid-level generalist data role in a metro attracts many qualified applicants.
Core data engineering skills transfer across industries, but investment-risk domain familiarity raises specificity moderately.
Explicit 3+ years plus required Python/SQL and cloud data tooling makes screening moderately strict.
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Build and maintain data pipelines, workflows, and analytics solutions to support risk management and quantitative research.
Develop scalable data assets and AI-assisted tools in collaboration with risk managers, quantitative analysts, and AI leads.
Translate business and risk management requirements into reliable, production-grade data solutions using Python, SQL, cloud data platforms, and visualization tools.
3+ years hands-on experience in Data Engineering, Data Analytics, or data-intensive applications.
Bachelor's or Master's degree in Computer Science, Data Science/Engineering, or related field.
Strong programming skills in Python and SQL.
Work Experience Required: 3+ years in relevant data engineering or analytics roles.
Experience working in cross-functional teams combining business stakeholders and engineers within financial or investment research domains.
Background in building scalable, production-grade data workflows with cloud platforms such as BigQuery, Snowflake, or GCP.
Demonstrated ability to translate complex business and risk workflows into technology solutions and collaborate with AI and research leads for innovative applications.