Top Senior Data Management Jobs in NYC, NY

The Senior Manager will lead B2B analytics efforts, drive data-driven strategies for marketing, optimize revenue growth, and enhance performance through advanced analytics frameworks and collaboration with stakeholders.

Lead strategy and delivery of enterprise streaming and social data products that serve as authoritative sources of truth. Ensure AI-ready, governed, semantically consistent data with metadata, lineage, observability, SLAs, and adoption plans. Partner with analytics, engineering, and commercial teams to define metrics, manage external data sources, enforce data contracts, and drive AI and predictive use cases for music consumption and audience insights.

Lead a small data engineering team partnering with Engagement Marketing to build scalable ETL/ELT pipelines, ensure data quality, define marketing metrics, integrate trusted data with ad platforms, and drive the data roadmap to enable measurement and growth.

Lead a team delivering actuarial statistical reporting, regulatory submissions, and analytics. Ensure compliance with bureau requirements, drive automated reporting and UAT for policy/claims systems (Guidewire, Solartis), partner with actuarial, technology, and vendors, and manage stakeholder/regulator interactions while mentoring staff and improving data governance and reporting processes.

Manages reporting and analytics operations, teams, workflows, critical business intelligence reports, and performance metrics. Leads data process improvements, requirements analysis, systems development, testing, reporting projects, and process re-engineering. Uses SQL, data warehouses, QlikView, Tableau, and related tools to support operational, financial, quality, compliance, and strategic decisions. Mentors staff, manages priorities, monitors risks, and partners with business leaders and clients.

Manage delivery of complex data pipelines in a hands-on role, leading a team of data engineers to develop scalable data solutions and improve analytics capabilities.

Design and maintain Looker/LookML data models and dashboards, perform end-to-end SQL analysis, ensure data quality with Data Engineering, build predictive models, and translate findings into actionable recommendations for cross-functional stakeholders.

Lead a small data engineering team while contributing hands-on to design, build, and maintain production data pipelines, warehouse, ETL, and BI stack. Own data reliability, governance, reporting (internal and actuarial), enable self-service analytics, consolidate analytics standards, and drive infrastructure/vendor decisions aligned to business goals.

The role involves leading the data governance team, managing IT projects, collaborating with stakeholders, and ensuring continuous improvement of processes. Responsibilities include strategic planning, client management, and optimizing resource utilization, while overseeing governance activities and departmental management.

Manage CVS Health's data partnerships and monetization strategy, source new partnerships, ensure operational success, and drive data product adoption across analytics teams.

Lead a hands-on data engineering team to build and scale production data pipelines, datasets, and observability for alternative-data products. Drive architecture, data modeling, QA, and AI-ready dataset design while coaching engineers, partnering with product and research, and owning operational excellence across Databricks, Airflow, SQL, and PySpark.

Lead marketing data automation and reporting efforts: build and maintain Power BI dashboards, automate reporting, integrate marketing and insights data, support AI data-readiness, and partner with Marketing, Finance, and Insights to deliver actionable analytics for planning and post-event analysis.

Own the end-to-end data products for Supply Chain: define roadmap, translate stakeholder needs into technical user stories, design scalable data models, oversee ELT/ETL pipelines and BI analytics, ensure data quality/governance, and lead Agile delivery while enabling predictive analytics.

Lead the Consumer Data Engineering team to build and deliver Gold-layer data products within a GCP Medallion architecture. Manage onshore engineers and offshore partners, align technical roadmaps with business priorities (personalization, attribution, omnichannel), ensure architecture standards, enforce code quality and testing, and run agile delivery of data product features.

Own and execute the data platform roadmap powering custodial ingestion, security master, and AI-ready data products. Lead and grow the data engineering team. Design canonical financial models across custodians, ensure data quality, reconciliation, lineage, and governance for regulated datasets, and support AI integrations.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

Lead and grow a data engineering team; define architecture and standards for ETL/ELT pipelines; hands-on design, code review, and development; ensure production reliability, monitoring, and incident response; partner with product and engineering on data roadmap.

Manage receipt-based data ingestion (OCR, transcription, email, account connections); ensure pipeline stability and data quality; translate business requirements into collection specs; partner with Engineering and Product; manage vendors, forecasting and budgets; produce KPI reporting; and support EU rollout and GDPR-aligned processes.

Lead and scale the enterprise data hub: manage the data warehouse, build reliable ingestion and transformation pipelines, implement governance and data modeling best practices, deliver GTM-ready metrics, enable AI/ML use cases, prioritize the data roadmap, and lead/mentor a data engineering team.

The Manager of Data Analytics will convert data into actionable insights for Product, Marketing, Content, and Finance teams, balancing speed and accuracy while collaborating with stakeholders.