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

Lead and grow Confido’s data engineering team, initially managing an analytics engineer while remaining hands-on with SQL, data models, pipelines, and code reviews. Own warehouse architecture, orchestration, tooling, data standards, monitoring, and metric definitions. Hire and develop engineers, prioritize the team’s roadmap, collaborate across departments, and improve data reliability, documentation, and reporting across the company.

Own end-to-end analytics engineering for AI and developer-experience telemetry, including instrumentation, production pipelines, governed metrics, monitoring, executive dashboards, and analysis. Define and validate new measures of AI productivity and ROI, communicate findings to senior stakeholders, and recommend improvements to tooling, processes, and investment. Collaborate with data engineering, applied AI, and data science teams while using AI coding agents to build and maintain pipelines and reporting.

Own AI and developer-experience analytics end to end, including telemetry instrumentation, production data pipelines, metric governance, executive dashboards, and analysis. Define trustworthy metrics from evolving signals, monitor pipeline quality, communicate insights to senior stakeholders, and recommend actions to improve AI ROI and developer productivity. Use AI coding agents to build and maintain pipelines, dashboards, and analyses while partnering with data engineering, applied AI, and data science teams.

Analyze Experian portfolio repayment performance, credit risk, underwriting and credit-limit changes, collections, funnel metrics, cohort LTV, and unit economics. Own partner analytics models, data definitions, reporting, alerting, and data quality monitoring. Collaborate with Product, Engineering, Partnerships, Finance, and Experian to guide roadmaps, evaluate experiments, identify risks, and support business reviews.

Serve as the strategic analytics partner to GTM leadership, connecting Finance, Revenue Operations, Product, Growth, and sales leaders. Build trusted analytics infrastructure, define metrics and reporting, develop scenario models, and translate complex business decisions into executive recommendations. Own recurring business reviews and the end-to-end view of sales performance across routes to market, helping leaders evaluate product, pricing, compensation, organizational, and market changes.

Supports enterprise data management and reporting for an investment management SaaS platform. Responsibilities include maintaining investment datasets, preparing reports and dashboards, performing data quality checks, documenting data governance standards, supporting ad hoc analytics, and archiving reports for compliance. The intern collaborates across teams and gains experience in financial data, investment analytics, and enterprise data governance.

Analyzes product, visitor, subscriber, acquisition, engagement, and retention data across Morningstar’s investor products. Builds KPI dashboards, automated reporting, forecasting and financial models, cohort and lifetime-value analyses, and SQL-based data pipelines. Partners with engineering and data teams to improve tracking, data infrastructure, and reporting accuracy. Uses insights to optimize pricing, marketing spend, customer acquisition, product engagement, and retention strategies.

Analyzes payments and banking data to identify trends, improve products, and enhance operational efficiency. Builds dashboards, conducts deep-dive and experimental analyses, monitors customer behavior, and delivers actionable insights to stakeholders. Partners with product managers, engineers, and leadership to define success metrics, ensure data quality, support fraud response, and improve analytics processes.

Analyze payment fraud data using SQL and dashboards, identify anomalous behavior and risk trends, evaluate fraud controls, and recommend improvements. Present findings to leadership and collaborate with Product, Data Science, Engineering, Customer Experience, and Risk Analytics teams. The intern will also document risk processes and responsibly use generative AI to improve workflow efficiency and quality.

Own and maintain dbt data models across design, testing, deployment, monitoring, and documentation. Translate ambiguous Marketing and Product needs into reliable data solutions, write design documents, support production issues, create runbooks and schema documentation, and coach junior engineers. The role uses SQL, Python, Snowflake, dbt, Dagster, and AWS while partnering closely with Marketing, Data, and MarTech teams.