Top Senior Data Management Jobs in Milwaukee, WI

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

Design, build, and validate SAP MDM repositories and logical data models. Configure MDM clients (console, data manager, import/syndication managers), create import/syndication maps, workflows, matching rules, and DB views; perform CLIX and server-level operations, INI configuration, and interface testing. Liaise with data providers, subscribers, and downstream systems and lead the team.

Lead design and implementation of SAP MDM repositories and data models; configure MDM clients, import/syndication maps, workflows, web services and CLIX operations; develop and test integrations with upstream/downstream systems; produce technical specifications and unit tests; coordinate with data providers and stakeholders.

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.

Oversee data ingestion and transformation, manage finance data sources, ensure scalability and reliability of data platforms, and collaborate with stakeholders to align data engineering with organizational goals.

Lead the enterprise data platform strategy and architecture, driving Snowflake/dbt-based platform evolution, self-service Data Mesh and medallion models. Build AI-ready pipelines, RAG systems, and observability/cost frameworks while managing a central data team, supporting federated BI, and executing hands-on technical work.

Lead data-driven strategies and analytics at PwC, motivating teams to improve business performance and decision-making through advanced techniques and collaboration.

As a Manager in Data Governance at PwC, you will lead data governance strategies, supervise teams, manage client accounts, analyze data for decision-making, and ensure compliance with standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Senior Manager in Data and Analytics Engineering at PwC, you'll design data architecture strategies for healthcare, mentor teams, influence stakeholders, and enhance healthcare operations through data solutions.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Lead implementation of end-to-end global data governance for domain areas (Product, Customer, Vendor). Run governance council, map data ownership, apply COBIT, drive change management and adoption, create roadmaps and scorecards, prototype and test solutions, represent data governance with cross-functional partners and AI governance, promote data literacy, and manage Precisely Automate Studio and ArcGIS licensing.

Lead a global, hands-on data engineering team building large-scale Databricks/PySpark pipelines, production datasets, customer-facing data feeds, AI-ready analytical datasets, and data quality/observability systems. Partner cross-functionally, use AI coding tools to accelerate delivery, and ensure operational excellence, reliability, and documentation for investor-facing products.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

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 a team of data engineers in designing and maintaining scalable data infrastructure and pipelines, ensuring data integrity and decision-making support.

The Data Engineering Manager will lead a team, driving technical growth, fostering collaboration, and ensuring effective data architecture and systems.

Manage and grow a data engineering team, overseeing the design and maintenance of data platforms using Snowflake, dbt, and Azure. Drive best practices in data management and ensure reliable data movement and quality.

The Senior Manager of Data Insights will lead a team to optimize data sharing and governance, develop data strategies, and deliver actionable business intelligence across Docker's operations.

Support data-driven operations by sourcing, integrating, validating, and transforming data; build and maintain dashboards, agency scorecards, and reports; analyze KPIs to identify improvement and cross-sell opportunities; measure campaign effectiveness; ensure data quality; partner with stakeholders and technology teams; support monthly reviews and ad-hoc analytics requests.

The Data Product Manager defines the reporting roadmap, manages KPI alignment, collaborates cross-functionally, ensures data quality, and leads continuous improvement in analytics 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.