Top Fintech Data & Analytics Jobs

The Senior Business Operations Analyst will focus on data-driven insights and operational strategies for Collections operations. Responsibilities include SQL analysis, Tableau reporting, and collaborative optimization with various teams to enhance efficiency and decision-making.

The Manager of Global Insights & Analytics leads MetLife's Global Customer Commitment Program, optimizing customer research and translating insights into strategies for improved customer experience and loyalty across markets.

The Director of Data Operations will build and lead the data operations function, drive operational excellence, and integrate AI with human expertise for data extraction and reporting in a dynamic environment.

The Senior Distressed Debt Reporter will break news on corporate restructurings, collaborate with journalists and analysts, and identify market trends in distressed debt.

The Staff Quantitative Developer will enhance risk analytics capabilities, develop pricing libraries and risk models, and mentor engineers while working with clients.

Build and maintain dbt models, write SQL to investigate and validate data, implement data quality checks, document lineage and metrics, resolve production issues (including on-call), and partner with analysts and business teams to deliver production-ready data products using modern warehouse tooling and AI-assisted frameworks.

The Enterprise Data Architect will design and govern data architecture, create enterprise data models, and ensure data consistency across various systems.

The Data Scientist will analyze financial data, develop predictive models, and communicate insights to drive business initiatives in financial services. Responsibilities include mentoring junior staff and collaborating with cross-functional teams to enhance analytic products.

Own and modernize Upsides analytics data platform: migrate pipelines, reduce cost, improve governance, design reusable modeling/orchestration patterns, deliver domain-critical data products, lead cross-functional initiatives, mentor engineers, and support ML and product teams.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.