Top Analysis Reporting Jobs in Ann Arbor, MI

Build and maintain web crawling and scraping pipelines to collect, transform, and deliver third-party healthcare data. Research sources, assess feasibility, implement extraction strategies, test for quality, and maintain scrapers when sources change. Participate in weekly standups and initial fundraising activity.

Organize and curate large volumes of raw information: extract key points from transcripts, maintain clean spreadsheets, curate links/resources, tag and categorize content, and follow or propose systems for information structure. Part-time remote role with initial 10 hours/week and potential to grow.

Build, operate, and modernize scalable data pipelines and warehouse environments for lottery reporting, analytics, and AI readiness. Improve ingestion, transformations, data models, contracts, observability, lineage, and onboarding patterns. Automate operations, troubleshoot production issues, mentor engineers, and support migration from legacy to cloud architectures while ensuring data quality, reliability, and regulatory continuity.

Lead capacity planning and commercial analytics: build and maintain capacity reporting, dashboards, and NetSuite inventory processes; enforce data governance and KPIs; partner with Sales, Finance, and Operations to enable data-driven commercial decisions and scalable growth.

Lead technical strategy and roadmap for an enterprise Databricks-based data platform. Drive governance, metadata, lineage, and semantic layer standards; build reusable data products; enable self-service analytics and scalable ML operations; mentor engineers and align platform investments with business outcomes.

Investigate and analyze suspected fraud cases, monitor and validate fraud strategies, develop countermeasures, evaluate risk scenarios, and collaborate cross-functionally to improve fraud detection and minimize false positives/negatives.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Lead a distributed SQL team and own the end-to-end service model for custom analytics: intake, scoping, delivery, QA, documentation, and SLAs. Serve as a customer-facing technical lead, build scalable workflows and request taxonomy, partner cross-functionally, and balance customer delivery with internal platform priorities.

Own and evolve the analytics platform: build and maintain dbt data models, govern semantic metrics in Lightdash, ensure data quality and observability, and partner with analysts and stakeholders to enable reliable self-service reporting.

Lead enterprise data platform strategy and architecture, define data standards and governance, drive Snowflake maturity, oversee legacy data migrations, design ingestion and DataOps patterns, and enable analytics, reporting, AI, and reusable data products across the organization.