Top Remote Analysis Reporting Jobs in Milwaukee, WI

Lead design of end-to-end analytics and data solutions, produce architecture diagrams and technical designs, guide delivery teams, run client discovery and requirements sessions, support pre-sales and proposals, build reusable frameworks, and manage and mentor a delivery team to ensure quality and alignment to architecture.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Partner with Content Finance stakeholders to build and own end-to-end data products and semantic models. Define core metrics, maintain documentation and tests, perform root-cause analysis, improve pipelines, and drive cross-functional alignment. Mentor teammates, champion data-as-product practices, and leverage GenAI tools to accelerate development.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Lead a team that manages master and client data operations, driving data quality, process improvements, automation, backlog and project execution, performance metrics, and cross-functional coordination to ensure timely, accurate data delivery and operational scalability.

Design, build, and maintain scalable data pipelines and integrations across AWS, Azure, and BigQuery. Normalize, aggregate, and ensure data quality for large web and transactional datasets, leveraging APIs, ETL/orchestration tools, and big-data technologies to support analytics and BI in an e-commerce environment.

Build and maintain scalable data pipelines for healthcare claims; implement de-identification and PII-reduction; ensure data quality and observability; integrate data stores and APIs; draft technical designs and documentation; gather product feedback and iterate quickly.

Lead performance investigations across the data product stack to diagnose and fix slow queries and UX issues. Design data architecture changes, build benchmarks, measure latency/throughput/cost (ClickHouse Cloud), prototype solutions, instrument monitoring, and partner with product and engineering to balance responsiveness, scalability, and cost.

Own pre-sales funnel analytics from lead to closed-won for EverHealth products. Build funnel conversion and pipeline-quality reporting, ICP and win/loss analysis, cohort and TAM analyses, Salesforce dashboards, and actionable insights for Sales, Marketing, and RevOps. Use AI tools to accelerate reporting and support GTM planning, quota modeling, and competitive intelligence.

Lead pricing strategy and analytics: develop pricing, packaging and monetization recommendations; build forward-looking revenue/volume models; maintain pricing algorithms and reporting; collaborate with Sales, Finance, Marketing and Data teams to implement pricing actions and standardize quantitative outputs.

Build and maintain scalable data pipelines and integrations using Databricks and Azure. Transform and model large structured and unstructured datasets to support reporting, analytics, and operational needs. Implement data quality, monitoring, and governance; troubleshoot pipeline performance; collaborate with stakeholders and mentor junior engineers.

Support review of source inventories and cross-reference authoritative port/waterway datasets; design and implement geodatabases and attribute schemas; produce and validate polygons, run topology checks, develop metadata and source lineage, document discrepancies, and support QA and incremental production deliveries.

Delineate, generalize, and prepare port, waterway, coastal, and maritime features using approved geometry and the Waterways Harmonization model. Create supplemental geometry, determine logical feature endpoints, preserve naming and source lineage, and produce publication-ready geospatial data for operational use, ingestion, maintenance, and archival while following schema and quality standards.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.