Top Senior Analysis Reporting Jobs in Milwaukee, WI

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

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.

Lead martech data strategy and governance to enable accurate, accessible customer data for segmentation, identity resolution, integrations, and personalized campaign activation across marketing platforms. Partner with Technology, Data & Analytics, Compliance, and business stakeholders to define requirements, drive data quality, and modernize platform integrations.

Lead and grow the Revenue Analytics team to deliver strategic sales and customer lifecycle insights. Build forecasting, pipeline, capacity, predictive models, and customer lifecycle analytics to improve revenue, retention, adoption, and expansion. Partner with GTM, Finance, Product, and Enterprise Data to create executive reporting, data governance, AI-enabled self-serve datasets, and integrated analytics that drive business outcomes and support a shift to consumption-based models.

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Partner with People teams to turn HR and org data into actionable insights. Build complex queries, statistical analyses, dashboards, and a centralized people data repository. Drive data governance, data quality, and stakeholder communication to support hiring, retention, productivity, and workforce decision-making.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Designs, builds, and optimizes cloud data solutions using Microsoft Fabric and Azure. Develops data pipelines, Lakehouse architectures, ELT/ETL processes, data models, semantic models, and Power BI reporting. Partners with stakeholders to define requirements and technical designs, supports platform migrations, and implements data quality, governance, security, and monitoring practices. The role also contributes to AI and machine learning data initiatives and requires strong SQL, Python, PySpark, and modern cloud data engineering expertise.

Analyze multi-channel fraud trends, develop data-driven insights and recommendations to prevent and detect fraud, evaluate controls and authentication strategies, support testing and implementation of fraud controls, and present executive-ready analytics to inform enterprise fraud strategy and prioritization.

Leads geospatial and GIS projects using the Esri platform to create maps, dashboards, spatial analyses, and field data solutions. Manages spatial data quality, project budgets, timelines, client relationships, proposals, and multidisciplinary deliverables. Advises clients on geospatial technologies and serves as a GIS project lead while supporting environmental assessments, infrastructure, permitting, and resource-planning projects.