Top Senior Analysis Reporting Jobs in Milwaukee, WI

Leads enterprise data strategy, platform architecture, Master Data Management, federated governance, data quality, compliance, and real-time analytics. Builds scalable data infrastructure, modernizes legacy pipelines, establishes operational data hubs, and enables secure data products across the enterprise. Oversees data engineering, architecture, MDM, governance, and embedded analytics teams while partnering with executives and business units to drive transformation, operational efficiency, AI readiness, and acquisition integration.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting identity graph construction, entity matching, and identity resolution. Partner with engineering and data science teams to productionize matching logic, evaluate match quality, monitor data quality and drift, and manage identity-provider relationships. Translate complex identity concepts into actionable insights for technical and non-technical stakeholders while helping define privacy-conscious product and data priorities.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Own and architect marketing and storefront analytics systems, including metric definitions, single sources of truth, multi-touch attribution, marketing mix modeling foundations, semantic models, and data health metrics. Partner with marketing, storefront, and leadership teams to improve acquisition measurement, budget allocation, data quality, and decision-making across international markets.

Design, build, and own scalable data pipelines and warehousing architecture from ingestion through analytics and incentive payouts. Lead data platform architecture, dbt modeling, data quality, lineage, observability, performance, and cost management. Partner with engineers and analysts on telematics accuracy and driver incentive reporting. Use AI coding tools daily to develop and review pipelines, SQL, and data models.

Manages a data engineering team responsible for scalable, reliable data pipelines, infrastructure, and analytics tooling. Provides mentorship, performance management, and technical leadership while coordinating with product, analytics, finance, compliance, engineering, and regulators. Prioritizes cross-functional projects, supports data architecture and governance, ensures performance and reliability, and operates within strict regulatory and audit requirements.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Owns enterprise workforce intelligence and employee listening strategy. Analyzes employee sentiment, workforce trends, talent, skills, organizational effectiveness, and work data to identify risks and opportunities. Develops dashboards, analytical methodologies, AI-enabled insight products, and self-service resources. Partners with HR leaders and data owners to translate complex quantitative and qualitative findings into actionable recommendations for workforce planning, succession, organization design, automation, and capability decisions.

Design, develop, and publish advanced interactive Power BI reports and dashboards; define data structures and ETL needs; build analytics models using Microsoft BI stack (Power BI, SSAS/AAS, dataflows); use DAX and SQL to implement complex logic; collaborate with data engineering and business partners; mentor junior developers and performance-tune reports.

Own sales analytics and forecasting as the trusted source of truth for sales leadership. Build predictive and machine learning models to assess deal close likelihood and slippage, maintain Salesforce and Tableau dashboards, improve data governance, and translate complex business questions into actionable GTM recommendations. Partner with Sales, Marketing, and Finance on reporting standards, strategic planning, QBRs, and executive insights.