Top Analysis Reporting Jobs in Milwaukee, WI

Analyze credit card sales and business performance using Databricks, SQL, Power BI, and related tools. Optimize lead prioritization and conversion, evaluate customer segments and markets, design A/B tests, and connect sales metrics to revenue, acquisition, and profitability. Present actionable insights and strategic recommendations to cross-functional stakeholders while supporting marketing and business strategy.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.

Lead consumer insights projects from brief development through research design, quantitative and qualitative analysis, storytelling, reporting, and client presentation. Manage client relationships, develop actionable recommendations, create surveys, oversee project plans, and coach junior team members. Serve as a strategic advisor by translating consumer and market data into business decisions.

Analyzes sales, cost, margin, pricing, rebate, supplier, and product-category data to produce management and ad hoc reports. Supports profitability modeling, market adjustments, supplier programs, distribution agreement compliance, and business trend analysis. Collaborates with Business Intelligence, Finance, Pricing, and Product Category Management teams, while maintaining reporting tools and methodologies. May mentor entry-level team members and support special projects.

Leads the data engineering function and manages a team of at least five employees. Owns enterprise data platform execution, scalable pipeline development, Medallion architecture implementation, DataOps, CI/CD, observability, reliability, stakeholder intake, and engineering standards. Partners with data architecture and application teams, mentors engineers, drives delivery, and develops future leaders.

Build trusted GTM business intelligence by defining commercial metrics, analyzing performance across Sales, Marketing, Customer Success, Product, and Finance, diagnosing root causes, and delivering executive recommendations. Partner with data engineering and operations teams to integrate Salesforce, product, support, marketing, billing, and financial data. Evaluate AI-powered analytics capabilities and improve self-service decision-making.

Supports TPG’s sustainability analytics team by creating dashboards, automating reporting, improving data processes, and contributing to emissions accounting, regulatory reporting, physical climate risk assessments, and LP engagement. The role requires advanced data visualization, analytics, and reporting skills, along with interest in responsible investing, climate, or sustainability.

Design, build, optimize, and operate enterprise cloud data platforms and Databricks ETL/ELT pipelines. Develop batch and streaming pipelines, data models, infrastructure-as-code solutions, BI dashboards, monitoring, and scalable ML models. Ensure platform reliability, governance, security, and compliance while collaborating with software, systems, product, and business teams.

Staff Data Analyst leading complex, cross-functional analytics initiatives and executive-level decision support. Responsibilities include defining metrics, building scalable data models and infrastructure with dbt, ensuring data quality and governance, developing analytical roadmaps, mentoring analysts, improving data literacy, and advising Sales, Marketing, Finance, and Operations stakeholders.

Leads and scales the Growth Product Insights data practice, managing and mentoring data analysts while contributing hands-on analytical work. The role drives proactive insights, experimentation, causal inference, cohort modeling, KPI development, and strategic recommendations for product and growth leaders. It also strengthens data quality and metric governance, partners with engineering teams on complex pipelines and event data, and integrates modern analytics and generative AI tools into team workflows.