Top Senior Data Management Jobs

Analyze large-scale data, build and validate models, improve processes and reporting, evaluate products and marketing initiatives, and consult on data systems. The role requires programming, strategic analysis, collaboration across concurrent projects, and leadership in a hybrid environment.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

As Senior Manager of Data Analysis, you will validate analytical tools, manage validation projects, assess quality and risks, and mentor junior staff.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Leads data analysis and AI-readiness initiatives for the personal loans business, including data governance, quality, automation, reporting, and analytics. Manages complex data analysis projects, improves processes and customer experiences, evaluates products and marketing initiatives, and collaborates across business units. Mentors junior analysts and contributes to data system design, business intelligence, and strategic testing.

Leads data analytics and AI-readiness initiatives for the personal loans business. Responsibilities include improving data governance and quality, automating monitoring controls, building data abstraction layers, analyzing large databases, evaluating products and processes, enhancing business intelligence, and mentoring junior analysts. The role involves cross-functional collaboration, strategic testing, reporting improvements, and consulting on data systems while managing concurrent projects.

Leads people analytics initiatives that support talent strategy and workforce decisions. Responsibilities include developing analytics frameworks, managing data projects, building AI-powered data products and self-service tools, analyzing workforce data, prototyping automation solutions, and delivering insights to HR stakeholders and senior leaders. The role also involves data strategy, dashboard development, project execution, and potentially people management.

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 enterprise healthcare data engineering strategy, cloud data platform modernization, warehouse and lakehouse architecture, ETL/ELT pipelines, streaming, interoperability, governance, data quality, and master data management. Manages data engineering, analytics, and platform teams while supporting AI/ML initiatives. Requires extensive healthcare data experience, leadership of large programs, cloud and data engineering expertise, knowledge of claims and clinical systems, and familiarity with HL7, FHIR, EHR platforms, and healthcare compliance.

Leads enterprise data risk measurement and analytics by defining metrics, building dashboards and data products, monitoring trends, translating policies into actionable insights, and presenting recommendations to executives. Manages junior analysts, drives cross-functional initiatives, supports data governance, applies AI to automate metrics, and performs analysis across large databases to improve processes, reporting, and business outcomes.

Manages data governance and pricing-related risks by overseeing processes, controls, reporting, documentation, and improvement initiatives. The role analyzes process data, identifies gaps, resolves policy-related issues, collaborates on enterprise projects, represents departmental interests, and trains junior staff and internal stakeholders. Responsibilities include maintaining procedures and diagrams, recommending process changes, supporting executive reporting, and ensuring adherence to data management and governance frameworks.

Own and operate Toast’s enterprise data governance program, including data domain ownership, governance council coordination, policy compliance, business glossary management, metadata standards, catalog adoption, maturity assessments, and data literacy training. The role partners with business stakeholders, data stewards, catalog teams, and leadership to improve data trust, compliance, discoverability, and quality. It requires governance, metadata, SQL, data catalog, program management, and workshop facilitation experience.

Leads a data analytics team developing AI automation strategies for card data. Responsibilities include validating analytical tools, assessing methodology quality and risk, researching emerging technologies, prototyping best practices, creating governance frameworks, communicating validation findings, and mentoring staff. The role requires expertise in data analysis, statistical and econometric modeling, large datasets, open-source data technologies, data governance, predictive analytics, and LLM-enabled strategies.

Manage and grow Ashby’s data analytics team while remaining hands-on with analytical work. The role includes hiring and coaching analysts, improving data practices, prioritizing requests, developing specialization, maintaining analytical quality, and guiding responsible AI adoption. The manager will work across raw and modeled data, dbt models, metric definitions, dashboards, and internal data products, partnering closely with business teams in a remote, high-growth B2B SaaS environment.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, and segmentation. Partners with Marketing and Finance to establish performance goals and translate complex data into actionable recommendations. Automates reporting using analytics platforms, marketing automation tools, experimentation methods, and AI-enabled workflows while identifying opportunities to grow and retain high-value consumers.

Manages governance, planning, integration, and execution for material, vendor, and customer master data. Leads SAP master data process design, requirements management, testing, user acceptance training, data analysis, and process improvement. Partners with change management teams on training materials and communications, manages assigned staff, and supports SAP S/4HANA MDG initiatives.

Leads enterprise data engineering strategy, data lake architecture, platform development, and scalable data product delivery. Manages a global data engineering team, oversees ELT/ETL pipelines, and advances data quality, governance, observability, orchestration, CI/CD, and cloud platform capabilities. Partners with business, operations, product, technology, vendors, and external stakeholders to prioritize logistics-focused initiatives supporting analytics, automation, real-time tracking, supply chain optimization, and AI-enabled decision-making.

Owns TSE’s enterprise data engineering foundation, including integrations, data lakes, pipelines, automation workflows, governance, identity resolution, and AI platform connectivity. The role supports CRM, ticketing, marketing, digital, and fan engagement systems while enabling personalized marketing, predictive analytics, and recommendation engines. It partners with technology, business teams, vendors, NFL, and MLS stakeholders to deliver reliable, governed, privacy-compliant data infrastructure.

Lead a globally distributed team of data, ML, and visualization engineers supporting Zoom’s billing, checkout, growth, and revenue analytics. Own data platform roadmaps, scalable pipelines, architecture, governance, access controls, reliability, and tooling decisions across Snowflake and Databricks. Partner with Finance, Product, and Data Science, while managing hiring, performance, mentorship, prioritization, and capacity planning.

Leads financial reporting and data analytics by developing dashboards, reporting packages, KPI insights, and data workflows. Oversees reporting governance, data quality, warehousing practices, automation, and infrastructure improvements. Manages and mentors analysts while partnering with executives, Finance, IT, and business units to translate complex data into actionable insights. Requires advanced Power BI and SQL skills, Databricks experience, and expertise in data management, reporting, and analytics.

Leads data analytics strategy and execution across business domains. Identifies data sources, develops advanced analytics and automation solutions, creates visualization strategies, and manages data quality and governance initiatives. Translates complex business problems into actionable insights, collaborates with stakeholders, ensures regulatory alignment, mentors analytics teams, and drives continuous process improvement. The role requires expertise in SQL, Python, statistical analysis, data visualization, enterprise data architecture, and stakeholder communication.

Leads advanced analytics for Mattel’s direct-to-consumer ecommerce business, including statistical modeling, experimentation, SQL-based data analysis, dashboard development, measurement strategy, and performance reporting. Partners with engineering, product, marketing, merchandising, and technology teams to translate complex data into business recommendations. The role also improves analytics architecture and governance while mentoring analysts and establishing best practices for scalable reporting and data-driven decision-making.

Integrate Procurement Data and AI teams by improving governed data products, implementing quality checks and schema enhancements, and preparing structured, documented, feature-ready data for AI and machine learning use cases. Translate governance standards into ML-accessible structures and ontologies, support AI-enabled analytics, and contribute to business intelligence reporting.

Leads business intelligence, AI, predictive analytics, data governance, and data architecture for a pharmaceutical manufacturing site. Builds dashboards, analytics pipelines, KPI frameworks, and predictive models; governs data quality, lineage, GxP compliance, FDA 21 CFR Part 11, and ALCOA+ standards. Integrates SCADA, MES, LIMS, ERP, and IoT data while developing and mentoring a technical team. Tracks business value through improvements in yield, uptime, cycle time, quality, energy efficiency, and operational performance.

Manages analytics projects using historical, current, and predictive data to support business decisions. Leads statistical analysis, experimentation, customer segmentation, forecasting, predictive modeling, dashboards, and reporting. Partners with marketing, operations, and senior leadership to deliver insights and recommendations. Supervises, mentors, and develops Data Strategies team members, manages project workloads, improves efficiency through automation, and maintains relationships with vendors and internal stakeholders.