Top Analysis Reporting Jobs in Ann Arbor, MI

Provide analytics, automation, and reporting to support compliance testing and monitoring. Collect, validate, reconcile, and analyze large data populations; develop repeatable testing routines, dashboards, and automated solutions; support sampling, exception analysis, reporting, and stakeholder requests to identify trends, risks, and remediation status.

Leads and develops a team of Conversion Data Analysts, owning workload prioritization, delivery quality, and performance outcomes. Uses KPIs and data to identify risks, guide decisions, and improve high-volume conversion workflows. Partners with project management, Engineering, Product, Support, and leadership to ensure accurate data mapping and successful customer onboarding. Requires people leadership, SQL, Excel, data validation, CRM, and conversion tooling experience.

Analyze operational data, build and maintain reporting models, KPIs, dashboards and scorecards. Provide actionable insights to improve network performance and customer experience. Prepare presentations for leadership, collaborate with cross-functional teams, and recommend operational efficiency improvements while maintaining BI best practices and continuing education.

Supports reporting, data analysis, dashboard development, data collection, quality assurance, and data warehousing initiatives. Cleans and analyzes data, generates actionable insights, communicates findings to nontechnical stakeholders, responds to data requests, and partners with KIPP program teams. The role also contributes to network events, team operations, and equity-focused culture development.

The Database Administrator III role requires U.S. citizenship and completion of all work within the continental United States, Alaska, or Hawaii. The provided description does not specify database administration responsibilities, tools, or technical qualifications. Compensation is $120,000 to $150,000 annually.

Supports IAA’s vehicle enhancement and make-ready program through recurring dashboards, baseline metrics, vendor and cost analysis, workflow documentation, process improvement, and leadership reporting. Tracks volume, cycle time, aging, completion, exceptions, SLA performance, utilization, and cost variances to identify operational risks and recommend improvements that support efficiency, margin, seller experience, and GTV growth.

Own the design, development, maintenance, and performance of Microsoft Fabric data pipelines, centralized semantic models, DAX measures, and documentation. Ensure data quality, consistency, and trusted marketing metrics across sources. Partner with data engineering and BI teams on deployments and production issues, investigate model discrepancies, perform analytical work, mentor apprentices, and contribute to applied AI initiatives.

Analyzes healthcare data using SAS, SQL, Excel, R, Python, Tableau, and related tools to identify trends, variances, outliers, and improvement opportunities. Creates reports, dashboards, tables, and visual presentations for technical and non-technical stakeholders. Supports healthcare intervention measurement, data entry, database functions, documentation, research, and policy compliance.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, data transformations, and Lakehouse architecture supporting analytics, machine learning, and operational use cases. Provides operational support for legacy C# .NET ingestion processes, troubleshoots pipelines, and promotes data governance, security, reliability, and documentation in a multi-tenant SaaS environment.

Lead the architecture and implementation of enterprise-scale GCP data ecosystems, including PB-scale platforms and AWS-to-GCP migrations. Establish data platform roadmaps, self-service architectures, governance, security, quality, and Infrastructure as Code standards. Architect solutions using GCP data, analytics, AI, streaming, and containerization technologies. Lead cross-functional engineering teams, mentor staff, advise executives, manage technical risks, and communicate the business value of data initiatives.