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Mid-level analytics role, metro location, known financial brand, and broad SQL/Python/BI requirements increase competition.
Analytics engineering skills are broadly transferable across industries despite financial services familiarity being preferred.
Explicit 3–6 year requirement and mandatory SQL/Python plus analytics engineering skills enforce strict filters.
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Own and maintain end-to-end data pipelines for predictive analytics workflows including monthly, quarterly, and ad hoc reporting deliverables with strong validation controls.
Translate business requirements into governed, reusable datasets and scalable reporting assets, working closely with data engineering, BI, and modeling teams.
Ensure operational readiness by building process documentation, runbooks, and maintaining continuity through backups, with proactive stakeholder communication.
3–6 years experience in data engineering, analytics engineering, reporting, or related analytics operations within financial services or data-driven environments.
Proficiency in SQL and Python; experience with data visualization/reporting tools like Power BI.
Bachelor's degree in Computer Science or quantitative field such as Math, Stats, or Data Science.
Willingness and ability to work in-office at least 3 days/week (hybrid model).
Strong analytical rigor with demonstrated experience in data quality validation, reconciliations, and control routines for recurring reporting or model outputs.
Experienced with modern data stack concepts including ELT patterns, dimensional modeling, semantic layers, and version-controlled analytics code.
Comfortable translating complex business requirements into technical data models and managing recurring deliverables with clear stakeholder communication and documentation discipline.