Top Data & Analytics Jobs in NYC, NY

Analyzes financial risk data and develops data transformation, reporting, dashboard, and visualization solutions for banking and capital markets clients. Supports quantitative analytics involving capital, liquidity, credit risk, and asset-liability management. The role also includes client collaboration, quality review, mentoring junior staff, process improvement, and automation of data movement and reporting.

Build and maintain production data pipelines, integrations, semantic models, AI agents, copilots, and automated workflows using business data. Partner with Sales, Marketing, RevOps, and Customer Success to deliver reliable analytics and automation solutions. Establish data quality, monitoring, evaluation, access controls, and deployment practices while enabling colleagues to adopt AI tools effectively.

Build and maintain analytics data systems and pipelines that capture, transform, and integrate product usage data at scale. Deliver accurate insights and visualizations to inform product decisions, collaborate cross-functionally, and apply AI tools to improve analytics and workflows.

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.

The Complex Data Exchange Specialist manages data integration processes, ensuring high-quality data exchange between clients and systems, while mentoring peers and driving process improvements.

Owns Pfizer GH&B’s AI-ready Snowflake data warehouse, data architecture, ontologies, governance, quality, lineage, access controls, and production reliability. The role supports AI/ML models, dashboards, and business-critical data applications; establishes testing, release, monitoring, and incident processes; directs contingent technical resources; and aligns business-specific data decisions with enterprise standards. Requires strong SQL, Python, Collibra, data warehousing, unstructured-data architecture, ontology, governance, and stakeholder-influence experience.

Own product analytics for embedded business areas, leading experimentation, root-cause analysis, business-case development, KPI reporting, and strategic recommendations. Build dashboards and Looker explores, partner with engineering teams to improve data models and reliability, present insights to leadership, use AI tools to improve analytics efficiency, and mentor analysts. The role requires advanced SQL, Python, statistical experimentation expertise, and experience with consumer fintech or similar B2C data.

Own business scorecards, dashboards, KPI reporting, and ad-hoc analyses focused on subscriptions, billing, revenue, retention, churn, engagement, and monetization. Translate ambiguous business questions into actionable recommendations, partner with Product, Finance, Marketing, Content, and Operations, and present executive-ready insights. Improve metric governance, data quality, and analytical practices across the organization.

Supports healthcare feasibility analytics by translating clinical and genetic cohort requirements into SQL queries, data transformations, visualizations, and actionable insights. Analyzes EMR, genomic, laboratory, and claims data; maintains dashboards and workflows; supports data quality assurance; and documents processes for post-sale development. Collaborates with clinical, research, business development, and technical teams while developing oncology and clinical genomics expertise.

Designs and maintains scalable data pipelines and infrastructure, establishes data governance and quality standards, and drives technical decisions across Headway’s data platform. Partners with Product, Engineering, Analytics, Data Science, Infrastructure, Finance, and Insurance Systems to improve architecture, performance, reliability, security, and AI-readiness. Ensures data is complete, traceable, trustworthy, and suitable for powering internal tools and AI-driven insights.