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Mid-level generalist data role, metro location, broad skillset and hybrid workplace drive high competition.
Analytics engineering skills are transferable across industries, though financial-services domain knowledge increases sensitivity.
Explicit 3–6 year requirement plus required SQL/Python and analytics engineering skills increase screening strictness.
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Own end-to-end analytics data pipelines and reporting deliverables supporting predictive analytics and recurring metrics validation.
Ensure data quality and operational readiness through strong controls, documentation, change management, and stakeholder communication.
Translate business requirements into reusable datasets, governed metrics, and scalable reporting assets for senior leadership and technical teams.
3–6 years of experience in data engineering, analytics, reporting or model operations within financial services or equivalent data-driven environment.
Proficiency in SQL and Python; experience with data visualization/reporting tools like Power BI.
Strong knowledge of data validation, reconciliations, and control routines for recurring reports/model outputs.
Working knowledge of modern data stack concepts: data warehouses/lakehouses, ELT, dimensional modeling, data lineage, version control, and analytics engineering.
Experienced in building and maintaining productionized analytics workflows with strong data governance and operational risk awareness.
Comfortable collaborating across business, data engineering, BI, and modeling teams to deliver controlled, documented, and automated reporting solutions.
Skilled in analytics engineering practices including modular coding, version control, automated testing, and scalable data model design.