Top Senior Analysis Reporting Jobs in Milwaukee, WI

Delivers financial and operational reporting, dashboards, forecasting, variance analysis, and profitability insights for Financial Services clients. Uses SQL, Power BI, and Excel to validate data, identify anomalies, support client implementations, and improve reporting processes. Partners cross-functionally with accounting, operations, analytics, FP&A, vendors, and leadership while maintaining data governance, documentation, and reporting standards.

Build and maintain production data pipelines that transform robot logs and telemetry into reliable, queryable datasets. Design Spark-based ETL workflows, orchestrate processing with Airflow or similar tools, manage gold-layer datasets, and implement data quality, schema evolution, monitoring, and metadata practices. Collaborate with robotics, hardware, reliability, analytics, and ML teams on data modeling, fleet insights, debugging, and root-cause analysis. The role may also contribute to Python, Java, or C++ libraries supporting data capture and consumption.

Establishes data quality, freshness, coverage, and delivery baselines across multiple brands; connects data gaps to commercial impact; identifies automation opportunities; defines SLAs; documents data standards, source specifications, governance policies, and requirements; and delivers executive-ready insights. The role partners with Data Operations, Sales, Customer Success, Product, and Engineering to improve data quality, automate workflows, manage governance, and track business outcomes.

Leads development and maintenance of actuarial data marts, datasets, extracts, reports, and analytical solutions supporting claims analysis, IBNR reserving, forecasting, trend analysis, and financial reporting. Partners with actuarial, finance, medical economics, and data teams to validate and reconcile healthcare data, automate refreshes, ensure auditability, and improve data quality. Provides technical leadership, requirements expertise, recommendations, training, and direction to junior staff and business partners.

Designs and leads production-grade batch, incremental, streaming, and real-time data pipelines across Microsoft Fabric and Azure. Builds Delta Lake medallion architectures, dimensional and Data Vault models, CDC and incremental-loading solutions, CI/CD automation, governance, security, observability, and data-quality controls. Supports migrations to Fabric, prepares governed data for AI and RAG workloads, and leads client-facing engineering workstreams, estimates, risk management, mentoring, and operational handover.

Analyze and maintain recruitment, onboarding, workforce, participant, and program-performance data for the NIH DataForce Service Program. Develop trackers, dashboards, reports, and briefing materials; monitor participant lifecycle activities; identify data-quality issues, risks, trends, and process improvements; and support recurring monthly and quarterly analyses. Collaborate with program managers, mentors, coordinators, and government stakeholders while protecting sensitive information and ensuring Section 508 accessibility compliance.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

Lead enterprise data-source integrations across process historian, logistics, and procurement platforms. Build Databricks lakehouse Bronze ingestion pipelines and Silver normalization models, configure Fivetran and ODBC connectors, establish cross-system mappings, and validate data architectures with operations, procurement, and transportation stakeholders.

Own PushPress’s data platform, including ingestion, orchestration, cloud warehouse performance, governance, privacy, data quality, monitoring, and AI-ready data foundations. Build reliable pipelines integrating product, payment, and third-party data; establish data contracts, access controls, auditability, and semantic definitions; support real-time analytics and trustworthy reporting. Partner with Engineering, Data, and AI teams while maintaining infrastructure, incident response, documentation, and operational runbooks.

Build and own production data infrastructure for a healthcare AI platform. Responsibilities include REST API ingestion, Azure Event Hubs streaming into Databricks, Delta Lake medallion pipelines, entity resolution, semantic metrics, data-quality instrumentation, reconciliation, schema-drift handling, and troubleshooting source-to-warehouse discrepancies. The role requires strong Python and SQL skills, independent pipeline ownership, and experience with Databricks, Spark, PySpark, streaming, and healthcare-related data is a plus.

Leads quantitative and qualitative market research on international development, AI adoption, institutional demand, funding, procurement, competition, and commercial opportunities. Evaluates AI and data service applications, develops market assessments and strategic recommendations, manages research initiatives, prepares executive presentations, and collaborates with leadership, technical, operational, and business development teams to support market entry and growth.