Top Senior Data & Analytics Jobs in NYC, NY

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.

Own people-data pipelines, organizational mapping, resource reporting, data transformations, reconciliations, and automated internal tools for Engineering, Product, and Design. Support headcount and resource planning, operating calendars, software capitalization, data governance, executive dashboards, and ad hoc workforce analysis. Partner with HRIS, Finance, Talent Acquisition, and EPD leadership to improve data integrity and operational decision-making.

Lead advanced data analysis and campaign measurement initiatives, developing statistical models, causal inference methods, dashboards, reporting solutions, and measurement frameworks. Collaborate cross-functionally to evaluate campaign effectiveness, validate incrementality, investigate anomalous results, and provide actionable recommendations. Present complex analytical findings and limitations to executives, influencing strategic and operational decisions. The role requires expertise in SQL, statistical modeling, data visualization, and data science programming languages.

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.

Lead the architecture and development of Vercel’s financial data platform supporting revenue recognition, consumption-based billing, forecasting, reporting, and compliance. Build reliable batch and real-time pipelines using Kafka, ClickHouse, Tinybird, and Snowflake; ensure data governance, lineage, auditability, and accuracy; partner with Finance, Accounting, RevOps, Security, and Audit; drive architecture and roadmap decisions; and mentor engineers.

Leads an analytics engineering team developing governed, scalable data models, semantic layers, and BI frameworks. Owns dbt, Snowflake, and Looker practices; establishes modeling, testing, documentation, metric, and governance standards; and enables trusted self-service analytics. Partners with data and business teams to translate requirements into technical solutions, deliver strategic data assets, track OKRs, and communicate insights to executives. Provides mentorship, resource allocation, project oversight, and hands-on technical leadership.

Lead analytics engineering by developing scalable data models, defining analytics requirements, establishing company-wide data quality standards, and delivering reliable data products. Partner with analysts, engineers, product managers, and other stakeholders to improve data accessibility and decision-making. The role requires advanced SQL, Snowflake, dbt, BI tools, data modeling, testing, documentation, version control, and CI/CD experience, along with fintech industry knowledge.

Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.