Top Senior Data Management Jobs in Minneapolis, MN

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead development and execution of data-driven strategies, manage project planning and delivery, mentor and coach team members, ensure quality and ethical standards, design cloud data architectures and governance frameworks, analyze complex datasets to produce actionable insights, and drive continuous improvement using advanced analytics and modern data technologies.

Lead development and implementation of data governance strategies, manage client accounts and deliverables, supervise and mentor teams, analyze data to inform business decisions, ensure compliance with firm standards, and identify improvements in data management processes.

Leads the Analytics and Reporting Services team supporting a Medicaid Enterprise Data Warehouse. Oversees reporting portfolio delivery, dashboards, data validation, governance, compliance, resource planning, and operational performance. Manages analysts and business intelligence professionals, translates stakeholder needs into analytics solutions, improves data quality and reporting automation, and advises government and executive stakeholders on reporting modernization and data-driven decision-making.

Lead and grow a data platform engineering team to build and maintain a petabyte-scale data lake and infrastructure. Drive platform roadmap, scale systems globally, collaborate cross-functionally, and embed best practices for security, privacy, and AI transformation.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Lead design and rollout of a fit-for-purpose data governance program: manage data lifecycle, implement governance frameworks and tools, drive data catalogs/lineage, upskill stewards, define KPIs, and partner cross-functionally to improve data quality, compliance, and usability.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Manage and improve corporate giving data systems: build and maintain Tableau/Excel dashboards, respond to ad hoc data requests, clean and govern community data, administer Bonterra/CyberGrants, and lead project management for giving programs and strategic initiatives while partnering with enterprise stakeholders.

Lead a team delivering Power Platform and AI-driven analytics solutions (Power BI, Power Apps, Power Automate, Copilot Studio, Snowflake). Guide data modeling, governance, automation, and AI-powered workflows; prioritize initiatives, modernize reporting, and mentor engineers to drive scalable, self-service analytics and business application adoption.

Lead and mentor a data engineering team to design, build, and support scalable ETL/ELT pipelines and cloud data platforms (Snowflake, ADF, Databricks). Define data engineering roadmap, enforce data governance and quality, implement CI/CD and infrastructure-as-code, and collaborate with stakeholders to deliver trusted data for analytics, reporting, automation, and business decision-making.

Owns enterprise-wide customer data governance, master data management, data quality, standards, lineage, security classifications, and compliance. Leads customer data domain strategy, golden-record definition, quality analysis using SQL, issue resolution, governance initiatives, stakeholder communication, change management, and regulatory alignment. Partners across business, sales, marketing, legal, privacy, and technical teams while managing data catalogs, integrations, audit trails, and governance training.

Lead enterprise Procure-to-Pay data initiatives, governance, and quality across Global Supply Management. Manage Data Analysts, analyze source systems (ERP, planning, WMS, shop floor), drive root-cause analysis, data migrations, validations, and process improvements to ensure accurate master and transactional data supporting planning, logistics, manufacturing, and digital transformation.

Lead a data platform and BI decision-support organization as a player-coach: set platform roadmap and standards, steer pod-based agile delivery, direct engineering work, design semantic/business-layer models, enable AI and automation (including prompt engineering), and partner with Commercial and SCOPs stakeholders to deliver adopted analytics products.

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.

Leads the strategy, architecture, and execution of an enterprise data platform supporting analytics, machine learning, operational systems, and AI applications. Manages and mentors data platform engineers while remaining hands-on with architecture, reliability, technical direction, and troubleshooting. Oversees pipelines, orchestration, governance, security, observability, data contracts, migrations, and platform operations. Partners cross-functionally to translate business needs into scalable technical solutions and communicates decisions to technical and non-technical stakeholders.

Lead a remote analytics team to deliver dashboards, data products, and decision-support tools. Oversee project plans, stakeholder engagement, data platform modernization, ETL/automation, Tableau/Power BI deployments, statistical modeling, QA, metadata, and staff mentoring to support federal social service programs.

Develop and implement commercial data acquisition and governance frameworks. Govern data standards, policies, cataloging, quality, lineage, privacy, and compliance. Evaluate and onboard external data sources, negotiate vendor agreements, and manage third-party data relationships. Lead governance platform deployment, training, change management, KPI development, reporting, and continuous improvement while coordinating Commercial Operations, Analytics, IT, and external partners.

Owns the end-to-end architecture, development, infrastructure, data pipelines, and Power BI reporting platform supporting data governance, informatics, analytics, auditing, and data quality. Leads and mentors a data engineering team, defines schemas and engineering standards, ensures security and HIPAA compliance, oversees testing and Agile delivery, manages stakeholder requirements, and remains hands-on with backend technologies, data models, Power BI, and DAX.

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 product strategy and roadmap for enterprise marketing data products (MDP, Customer 360, conversational analytics). Coordinate data engineering delivery, design scalable data models, improve data governance and quality, modernize cloud-native architectures, and enable AI-ready customer intelligence to support measurement, personalization, and commercial decision-making.

Serve as the analytics bridge between marketing/operations and data teams: translate business questions and KPIs into clear product requirements, own delivery of treated tables, semantic layers, reports and dashboards, align stakeholders on a single source of truth, run gap analyses, document processes, and ensure delivered data products drive decision-making and adoption.