Top Data & Analytics Jobs in NYC, NY

Leads third-party advertising measurement strategy, partner governance, benchmarking, meta-analyses, and analytics innovation. Advises senior executives, influences cross-functional roadmaps, develops measurement playbooks, presents industry thought leadership, and builds measurement capabilities across Sales, Product, Marketing, and Partnerships. The role requires advanced statistical and causal inference expertise, hands-on SQL and visualization skills, and more than ten years of advanced analytics and measurement experience.

Support private markets data operations by processing LP documents, triaging automated exceptions, ensuring data quality, and analyzing trends to inform product and process improvements. Coordinate with onshore/offshore teams and partner with Product and Engineering on operational needs.

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.

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.

Build and maintain dbt data models and pipelines, implement real-time ingestion with PySpark and AWS Glue, and apply software engineering practices to analytics development. Collaborate with data scientists, stakeholders, IT teams, and data stewards on data quality, governance, integrations, and deployment. Translate requirements into Jira stories, manage milestones, review Git pull requests, and support enterprise data activation and advanced analytics.

Develop and optimize advanced SQL transformations, stored procedures, ETL workflows, and data-quality processes for large Medicaid claims and health-related social needs datasets. Build reproducible, documented data pipelines; calculate healthcare quality, equity, and disparity metrics; conduct code reviews; and collaborate with stakeholders to turn complex data into visualizations and policy insights.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

Lead and grow a data engineering team responsible for allocating cloud costs to products and customers. Own reproducible, versioned metric publication for Finance, platform-team cost contracts, coverage onboarding, identity mapping, and cost-to-revenue joins. Drive roadmap delivery across teams, improve cost accuracy and coverage, reduce key-person risk, and use AI tooling to increase engineering productivity.

Leads enterprise field research and insight generation focused on financial advisors, clients, and consumers. Develops learning agendas, performs statistical analysis, integrates internal and syndicated datasets, and translates multi-source findings into actionable strategies. Partners cross-functionally with Marketing, Product, Digital, and Field teams; manages vendors and budgets; improves research practices; mentors analytical talent; and supports AI adoption in data synthesis.

Develop and maintain scalable data pipelines and secure infrastructure for multimodal synthetic data, AI evaluation, real-time model monitoring, and clinical research data. Responsibilities include data transformation, preprocessing, quality assurance, database management, privacy compliance, interactive dashboards, pipeline documentation, and scientific dissemination.