Top Fintech Data & Analytics Jobs in New York City, NY

Own and build January’s foundational data systems, including end-to-end pipelines, data quality standards, legacy migrations, recovery processes, and documentation. Partner with engineers, analysts, operations, and product teams to establish trusted, scalable data infrastructure. Prioritize high-impact improvements, manage stakeholder needs, define ownership boundaries, and use AI to create increasingly self-healing systems.

Leads global network economics and value optimization initiatives through data-driven pricing, interchange, revenue, and profitability analysis. Develops predictive models, simulations, segmentation frameworks, reporting, visualizations, data pipelines, and scalable analytics tools using large datasets. Partners with global and regional pricing teams, translates stakeholder needs into analytical solutions, presents actionable findings, manages project delivery, and mentors junior colleagues.

Supports private-market document processing, data-quality reconciliation, exception triage, workflow management, and offshore team coordination. Serves as a subject-matter expert on private-market data, analyzes trends, documents operational logic, identifies process improvements, and collaborates with Product, Data, and Engineering teams on roadmap and systemic fixes. Provides operational summaries and data-quality reports to support client-facing teams.

Summer interns support business analytics and digital transformation initiatives by building dashboards, analyzing datasets, developing visualizations, researching digital product enhancements, and assisting with automation and process improvement. The role involves translating data into actionable insights, collaborating with professionals, and using technology to support business decisions. Candidates must be enrolled in an accredited four-year institution and graduate between December 2027 and May 2030.

Own Tomo’s data function, strategy, roadmap, platform health, and cross-functional measurement. Build and lead teams spanning data science, analytics, business intelligence, and data engineering. Design experiments, marketing attribution models, SQL queries, and dbt models while maintaining reliable data infrastructure. Partner with Product, Engineering, and executives to improve instrumentation, translate business questions into insights, and establish scalable data processes.

Lead analytics and reporting for alternative credit portfolios: monitor and analyze investment, portfolio and fund performance; produce monthly/quarterly/ad hoc client reports; maintain data integrity and reporting processes; support investor due diligence and custom requests; coordinate with valuation, fund accounting, investor relations, compliance and portfolio managers.

Own the architecture and standards of the company’s dbt project, building scalable models across staging, intermediate, and mart layers. Partner with product, design, and engineering teams to evolve data models, validate metrics, and maintain data quality. Develop AI-assisted testing, documentation, and access workflows; collaborate on Segment, Fivetran, and Airflow pipelines; and drive privacy, governance, and warehouse best practices.

Build and lead large-scale cloud data engineering initiatives, including cross-cloud migrations, lakehouse architecture, scalable data pipelines, data modeling, and real-time data solutions. Use Python, Spark, Databricks, Snowflake, SQL, and relational and NoSQL databases. Independently make architectural decisions, evaluate platform choices, mentor engineers, collaborate with product and software teams, and deliver reliable data platforms powering personalized travel experiences.

Lead analytics engineering efforts to design and build dbt models, maintain a semantic/metrics layer, ensure Snowflake performance, establish modeling/testing/documentation standards, partner with Data Science/Product/Engineering, and deliver native warehouse data products to customers.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.