Top Analytics Jobs in Milwaukee, WI

As a Director in the Data & Analytics team, you will lead client engagements, manage projects, develop strategies, create proposals, and mentor team members in data-driven initiatives.

Manage client data and analytics projects to design and implement data strategies, automation, visualizations, and predictive analytics. Translate data into actionable insights, lead cross-functional stakeholder engagement, scope and monitor budgets, and develop internal tools and templates. Apply finance/CPA knowledge where relevant and deliver client-facing presentations.

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 payments analytics by building scalable reporting, causal inference and ML models, designing experiments, partnering with product/engineering/finance to optimize payment processing, reconciliation, and settlement, and translating results into executive recommendations to drive global payments improvements.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

Own marketing channel performance tracking, reporting, and analysis. Design and analyze experiments (A/B, incrementality), build dashboards in Looker/Tableau, support LTV and forecasting models, identify measurement gaps, and translate analysis into clear recommendations for Growth, Marketing, and Finance stakeholders.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Design and build a modern data foundation: develop and scale ETL/ELT pipelines, dbt models and macros, semantic/metric layers, and analytical apps. Ensure pipeline reliability and observability, document systems, gather stakeholder requirements, and contribute to cross-functional analytics and ML initiatives.

Six-month internship to collect, clean, and analyze technical service, lab, and customer case data; build structured datasets; create visualizations and Power BI dashboards; support AI-assisted data processing and automation; present findings and collaborate with engineers and scientists to inform product and reliability decisions.

The RevOps Analytics Engineer is responsible for maintaining data infrastructure, building reliable data systems, and supporting revenue analytics through effective data modeling and ETL/ELT processes.

Lead enterprise analytics initiatives: translate business requirements into scalable data models and transformations; design enterprise dashboards and SSAS/SSRS/Qlik reports; perform advanced SQL analysis; resolve cross-system data issues (including Workday integration); mentor analysts; enforce data governance and reporting standards; manage multiple analytics projects and documentation.

Lead a team to develop data strategy and insights across multiple business functions, driving analytics evolution and ensuring data quality and governance.

The Senior Analytics Engineer will build and maintain data foundations for corporate reporting, translating business requirements into data models, ensuring consistent metric definitions and investigating data discrepancies for reliable reporting.

As a Revenue Analytics Manager, you will own reporting for Sales, Marketing, and Customer Success, build dashboards, automate workflows, and ensure data quality.

Lead analytics for Community Marketing programs: define measurement frameworks, build Power BI dashboards, run analyses across referrals/events/COI, deliver executive insights, forecast leads/opportunities, and partner cross-functionally to optimize program performance and reporting.

Own the Finance & Operations data layer: ingest and model data from systems (NetSuite, Ramp, Pigment) into Snowflake/Omni, ensure data quality and metric governance, build dashboards and reports, collaborate with Finance/Ops stakeholders, and automate reconciliation and reporting workflows.

Senior AWS Analytics Engineer. Posting contains no detailed responsibilities or qualifications beyond the job title.

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.

Design and deliver reporting, dashboards, and analytics for higher education advancement and constituent data. Gather requirements, define KPIs, perform SQL-based analysis and data validation, and build actionable Tableau visualizations. Advise clients, run workshops, collaborate with data engineering/CRM teams, document business rules and tests, and support adoption and analytics modernization initiatives within a consulting environment.

Develop and maintain value-based pro forma scenario and negotiation models, automate and standardize modeling, provide hands-on analytic support for IDN negotiations, collaborate with business partners on analytic solutions, and communicate results to stakeholders.

Support product analytics for the NPR App, site, and video by designing measurement plans, implementing and QCing tags, querying BigQuery, building dashboards, diagnosing data issues, automating reports, and producing analyses and documentation for stakeholders.

Lead mobile analytics and experimentation for the NPR App: design and analyze experiments, extract insights from complex datasets, build reports and visualizations, write advanced SQL, partner with product and newsroom stakeholders, train colleagues, and deliver actionable recommendations to grow audience and revenue.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

The Senior Analyst, Revenue Operations is responsible for delivering analytical insights, enhancing forecasting accuracy, and optimizing revenue processes while collaborating with various departments.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.