Top Data & Analytics Jobs in Milwaukee, WI

Build and support GCP-based data pipelines, ETL/ELT processes, data models, analytics dashboards, and AI/ML solutions. Assist with BigQuery optimization, batch and streaming ingestion, Vertex AI and Gemini experimentation, MLOps, prompt engineering, embeddings, RAG, governance, data quality, and security. Participate in client requirements gathering, technical implementation, documentation, and collaboration with senior engineers and architects.

Build and optimize batch and streaming data pipelines, ETL/ELT processes, data models, and analytics solutions on Google Cloud. Support Vertex AI and Gemini-based ML solutions, MLOps, prompt engineering, embeddings, and RAG experimentation. Participate in client requirements gathering, architecture implementation, data governance, quality validation, security, monitoring, and documentation while collaborating with senior engineers and architects.

Leads pricing strategy, business intelligence, reporting, data governance, and product data management. Manages the Pricing and Data Analytics team, develops pricing controls, analyzes profitability and market trends, establishes reporting standards, and creates Power BI dashboards and executive scorecards. Partners with sales, procurement, IT, finance, marketing, quality, and leadership to support strategic decisions, continuous improvement, and company-wide data accuracy.

Supports enterprise business intelligence and data strategy initiatives by developing analytics solutions, dashboards, reports, data pipelines, and multi-source datasets. Troubleshoots data issues, performs business analysis, and helps deliver scalable self-service reporting using ETL/ELT, data modeling, SQL, scripting, and visualization tools.

Leads enterprise metadata management, data catalog, lineage, business glossary, and governance programs. Establishes standards, stewardship processes, catalog and lineage capabilities, impact analysis, compliance support, and data discovery practices. Partners with governance, engineering, architecture, analytics, security, and business teams to improve metadata quality, data trust, regulatory readiness, self-service adoption, and AI and analytics readiness.

Lead enterprise product data governance strategy, standards, ownership models, quality KPIs, and executive reporting. Drive organizational change, process maturity, continuous improvement, and cross-functional alignment across Engineering, Product Management, Supply Chain, Operations, IT, and Data Management. Support Windchill adoption, MBOM expansion, ERP readiness, and Digital Thread transformation while improving data quality, accountability, adoption, and business outcomes.

Manages master data, validates data quality, analyzes information from multiple sources, develops statistical models and reports, troubleshoots reporting databases, supports data integrity initiatives, and trains users on dashboards. The role also translates reporting requirements, recommends software solutions, and communicates findings to stakeholders.

Analyzes enterprise fraud data across deposits, digital, payments, authentication, and account lifecycle domains. Evaluates fraud decisioning, alerts, losses, false positives, customer friction, queue performance, and operational efficiency. Develops measurement frameworks, dashboards, performance metrics, and executive insights; conducts root-cause and trend analysis; quantifies benefits, risks, costs, and tradeoffs; and partners with Fraud Strategy, Risk, Operations, Technology, and AI/ML teams to improve fraud controls and prioritize investments.

Leads business analysis activities across project lifecycles, including stakeholder communication, requirements validation, application testing, defect resolution, reporting, data analysis, and process improvement. The role partners with developers and cross-functional teams to improve application development practices, troubleshoot issues, and enhance operational quality and efficiency within a financial services environment.

Designs and leads scalable cloud data architectures for high-volume industrial IoT and telematics systems. Responsibilities include data modeling, production datastore design, data integration, governance enablement, performance tuning, reliability, and cloud platform strategy. The role collaborates with engineering, product, ML, and business stakeholders; evaluates architectural improvements; and mentors engineers through design and code reviews. Azure experience is preferred, with additional emphasis on SQL Server, NoSQL, data pipelines, lakehouse patterns, infrastructure as code, and AI/ML enablement.

Leads business and agile analysis for complex product and technology initiatives. Elicits and documents requirements, user stories, acceptance criteria, data mappings, and process improvements; supports backlog prioritization, product roadmaps, sprint planning, UAT, release decisions, defect resolution, and change management. Collaborates with business, product, design, IT, QA, and technical teams while promoting business analysis standards and managing project workstreams.