Top Analysis Reporting Jobs in Milwaukee, WI

Analyzes the end-to-end digital purchasing journey across Web and App platforms. Leads A/B testing and experimentation, builds self-service reporting, measures campaign impact, identifies customer experience opportunities, and communicates insights to product, digital experience, analytics, and senior leadership stakeholders.

Administer and optimize Microsoft SQL Server and Oracle databases in a federal enterprise environment. Responsibilities include installation, patching, monitoring, backup and recovery, performance tuning, access management, security auditing, cloud migration support, SQL development, documentation, ServiceNow incident response, root cause analysis, and on-call support. The role requires at least four years of database administration experience and the ability to obtain Public Trust clearance.

Own the end-to-end architecture and roadmap for MyFitnessPal’s product data stack. Coordinate data engineering, business intelligence, product analytics, and backend teams; establish data movement and standardization practices; lead tool evaluations and platform migrations; implement governance, PII handling, deletion workflows, access controls, and cost optimization; and document architecture standards across the organization.

Leads PBM data integration delivery across claims, eligibility, accumulator, and encounter systems. Profiles and validates data, writes advanced SQL, resolves reconciliation discrepancies, investigates production issues, and partners with engineering on scalable healthcare integration architecture. Owns timelines, risks, vendor dependencies, testing, UAT, migration, rollback planning, executive reporting, and post-deployment support. Requires deep PBM claims-processing expertise, data architecture experience, and hands-on knowledge of ETL, secure file transfer, job scheduling, and healthcare data standards.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Define and lead the company’s data architecture strategy, roadmap, and standards. Drive the transition from batch to real-time data platforms, guide decisions on governance, security, quality, scalability, and reliability, and identify practical AI applications. Partner with executive, product, and engineering leaders, establish design reviews, influence cross-functional decisions, and lead a global team of senior data architects.

Architects and implements end-to-end Microsoft Fabric and Azure data solutions for insurance datasets. Designs data models, ingestion pipelines, Lakehouse architectures, governance controls, security models, and scalable analytics platforms. Collaborates with governance, cloud engineering, security, business, and analytics teams to ensure compliance, performance, reliability, and cost efficiency. Provides architectural direction, leads design reviews, evaluates Fabric capabilities, and guides data engineers, analysts, and BI developers.

Executes eDiscovery analytics and AI-assisted workflows in Relativity and related platforms, including TAR, continuous active learning, generative AI classifiers, validation sampling, statistics, and structured analytics. Maintains accurate matter records, tracking, time entries, audit trails, and quality controls. Provides weekend coverage, monitors request queues, supports deadline-driven projects, documents handoffs, and escalates sensitive issues. The role includes structured training and progression toward an eDiscovery consultant track.

Analyze business data using SQL, BigQuery, Tableau, Python, and related tools to identify trends, build dashboards, maintain KPIs, and deliver actionable insights. Translate ambiguous business questions into reporting, automation, AI-powered workflows, and technical solutions. Partner with technical and business stakeholders, improve data quality and storytelling, and explore emerging AI technologies such as agentic workflows and LLM-powered automation.

Lead and grow data engineering and analytics engineering teams while overseeing data platform modernization, BigQuery and Looker migrations, reusable dbt models, self-service capabilities, governance, reliability, and data foundations for AI and ML. Partner with technical and business stakeholders to prioritize roadmaps, manage hiring and performance, improve adoption, and protect sensitive healthcare data.