Top Senior Data Management Jobs in NYC, NY

Lead large-scale data engineering initiatives, designing scalable cloud data platforms, pipelines, and architectures. Use Python, Spark, SQL, Databricks, Snowflake, AWS, relational and NoSQL databases to deliver resilient data solutions. Mentor engineers, analysts, and data scientists; establish engineering standards; evaluate platform choices; and collaborate with product and software teams to support enterprise data transformation and AI capabilities.

Leads data analysis for Capital One Travel, partnering with product and engineering teams to define KPIs, build dashboards, conduct deep-dive analyses, design A/B tests, troubleshoot tracking and data issues, and improve data models. The role supports product decisions, operational improvements, and accessible, reliable data across complex travel databases. It also requires collaboration with stakeholders and hands-on use of AI-powered tools for data and code investigation.

Lead data analytics for the Hotels Travel Agency team: define KPIs, build dashboards, run A/B tests, conduct deep-dive analyses, troubleshoot tracking and data quality, and partner with product and engineering to improve data models and accessibility.

Leads client-facing data validation and risk workstreams, developing reliable data pipelines, integrations, transformations, and validation controls. Manages project plans, budgets, timelines, deliverables, and team performance while analyzing data structures and system interactions to identify risks. Coaches team members, collaborates with stakeholders, improves database and query performance, promotes automation and AI-enabled tools, and ensures technical standards, documentation, and independence requirements are maintained.

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.

Manage data analytics for PULSE Risk & Compliance by analyzing large datasets, identifying transaction anomalies and risk exposure, validating models, improving reporting and processes, and supporting compliant data strategies. The role involves Python-based programming, BI, database and data-storage design, strategic testing, cross-functional collaboration, project leadership, and people management in a hybrid environment.

Leads data analytics strategy, modeling, testing, reporting, and process improvements across business units. Mines complex databases, evaluates products and processes, improves operational efficiency, and consults on data systems. Manages and mentors junior analysts while supporting projects through the full lifecycle. The role emphasizes business intelligence, programming, strategic analysis, and collaboration in a fast-paced environment.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, segmentation, and LTV analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and deliver actionable recommendations. Requires strong SQL and Python skills, causal measurement expertise, marketing platform experience, and the ability to communicate insights to senior stakeholders.

Leads data strategy and analytics for digital media clients, translating complex measurement and modeling results into actionable marketing recommendations. Responsibilities include designing media experiments, managing custom analyses, supporting measurement roadmaps, owning client data technology stacks, developing clean-room insights, and presenting client-ready narratives. The role also mentors analysts, manages potential direct reports, collaborates cross-functionally, and advances analytics solutions and departmental capabilities.

Lead and grow Stash’s Data Engineering team while owning the AWS data platform roadmap. Improve reliability, observability, incident response, automation, CI/CD, infrastructure as code, data quality, security, and cost management. Guide ingestion, distributed processing, storage, warehouse delivery, and real-time pipelines. Partner with engineering, SRE, analytics, data science, and business stakeholders to establish ownership, reliable data contracts, and maintainable architecture.

Leads the design, development, migration, and deployment of scalable data pipelines and cloud data solutions. Establishes reusable ingestion, transformation, quality, governance, compliance, and orchestration processes. Partners with business and BI stakeholders to define data requirements, support financial and operational reporting, and enable AI use cases. Requires expertise in cloud and big data technologies, DevOps, CI/CD, data management, and governance, with preferred experience in Databricks, Snowflake, data warehousing, and finance-domain data.

Leads marketing data analysis, audience segmentation, campaign targeting, and performance insights using SAS, SQL, Python, Databricks, and Salesforce Data Cloud. Translates marketing strategies into campaign specifications, develops and activates customer audiences, validates targeting logic, and communicates recommendations to marketing, business, compliance, and technology stakeholders. The role improves targeting methodologies, supports campaign execution, identifies growth opportunities, and provides specialized business insights while ensuring governance and regulatory alignment.

Leads data governance and management activities, including data stewardship, data quality monitoring, risk and incident remediation, data lineage, controls, third-party compliance, and roadmap development. Partners with business, technology, and product teams to implement governance strategies, address data risks, and improve data consistency and usability across the organization.

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time pipelines. It also requires influencing cross-functional teams, mentoring data professionals, establishing engineering standards, and communicating technical outcomes to stakeholders.

Leads enterprise data governance initiatives covering data quality, metadata management, critical data elements, cataloging, lineage, profiling, and monitoring. Develops and executes data management roadmaps, strengthens regulatory data controls, partners with business, risk, legal, and technology teams, and manages a distributed U.S. and India team. Oversees governance platforms, stakeholder engagement, training, reporting, and continuous improvement in a highly regulated banking environment.

Own customer analytics and data infrastructure across Shopify, CDP, CRM, and data warehouse systems. Build segmentation, dashboards, retention and lifetime value analyses, and predictive models using SQL and Python. Ensure data quality, governance, identity resolution, and audience activation while partnering with marketing, CRM, ecommerce, merchandising, and leadership. Present actionable customer insights and recommendations to support personalization, campaign measurement, and business decisions.

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.

Lead product analytics for Enterprise Data products, validating analytical tools, assessing methodology risks, researching emerging practices, and translating complex data into actionable insights. Manage validation projects, communicate findings through reports and presentations, and coach junior staff. The role requires advanced data analysis, statistical or econometric modeling, predictive analytics, large-dataset analysis, data governance, and people leadership.

Leads enterprise analytical-tool validation, including framework refinement, project execution, methodology risk assessment, technical research, prototyping, and validation reporting. Partners with cross-functional leaders to influence business strategy, solves complex analytical problems, and coaches junior staff. Requires substantial data analytics, open-source technology, statistical modeling, and people-management experience.

Leads people analytics initiatives covering workforce planning, retention, recruiting, engagement, organizational effectiveness, and productivity. Develops executive dashboards and KPI frameworks using workforce and business data, delivers diagnostic and predictive analyses, ensures data governance, and translates findings into actionable recommendations for HR and business leaders. Partners cross-functionally to improve reporting, analytics infrastructure, and data-driven decision-making.

Senior Data Manager acquires, validates, integrates, protects, and manages enterprise data throughout its lifecycle. The role develops ETL solutions, data architectures, models, stored procedures, reporting datasets, and visualizations using Databricks, SQL, and cloud technologies. Responsibilities include translating business needs into technical requirements, leading data projects through deployment, analyzing complex data, improving processes, and supporting business decisions through accessible insights.

Leads and manages a technical team building and operating enterprise-scale GCP data pipelines. Oversees ingestion, transformation, curation, exposure, governance, reliability, cost optimization, and migration of data workloads. Provides hands-on technical leadership in SQL, Python, Spark, data modeling, and GCP architecture while partnering with architects, platform teams, governance stakeholders, and business users. The role also manages operational metrics, compliance requirements, emergency assignments, and team development.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Leads the design, delivery, and optimization of data architecture, pipelines, semantic layers, data quality frameworks, and Tableau BI platforms. Partners with stakeholders to manage requirements, project plans, sprint execution, monitoring, governance, and release delivery. Guides data engineers, manages team members, and supports AI-enabled analytics capabilities. Requires strong SQL, Spark, PySpark, SSIS, cloud data platform, CI/CD, Tableau infrastructure, and data engineering expertise.

Leads and grows the data engineering team while owning enterprise data architecture, ingestion, modeling, semantic layers, governance, secure APIs, and reliable platform outcomes. Partners with finance, operations, sales, marketing, and product to deliver governed, traceable data for products and AI agents. Sets the roadmap, manages vendors and costs, reviews architecture, contributes code, and delivers cross-functional data platform projects.