Top Remote Analysis Reporting Jobs in Ann Arbor, MI

Performs audit-focused data extraction, analysis, and reporting using SQL, R, Python, SAS, Posit, and Snowflake. Supports internal and external audits, legal, compliance, and investigations by transforming complex datasets, identifying risks and trends, reviewing data models and process flows, and providing code feedback. Requires 5+ years of data analysis experience, a relevant bachelor’s degree, and strong querying and project management skills.

Develop and maintain data pipelines, datasets, dashboards, paginated reports, PowerApps solutions, Power Automate workflows, and Databricks data engineering processes. Translate ambiguous business needs into scalable analytics solutions, write complex SQL, support reporting and data modernization, and contribute to AI/ML initiatives. Collaborate with technical and non-technical stakeholders while delivering actionable insights and automated workflows.

Analyze case, client, and financial data to build Power BI dashboards, executive and client reporting, recurring Excel deliverables, SQL analyses, and lightweight automations. The role investigates discrepancies, supports ad hoc business questions, documents metric definitions, and partners with operations, sales, finance, and leadership to improve reporting and reduce manual work.

Coordinates Oracle EPM Cloud data governance, metadata management, integrity reviews, reconciliations, restatements, reporting controls, audit support, and system enhancements. Partners with Finance, Accounting, IT, and business stakeholders to resolve data variances, document requirements, support SOX compliance, maintain reporting outputs, and improve enterprise financial reporting processes.

Build and maintain dimensional data models in dbt and Snowflake for finance, operations, product, sales, and other business functions. Translate business questions into reliable data solutions, own model lifecycles, establish modeling and testing standards, optimize Snowflake performance and costs, collaborate with data engineering teams, review code, mentor colleagues, and communicate data definitions and tradeoffs to business leaders.

Owns the CX Operations data foundation in Snowflake by designing architecture, governance, data quality standards, and reusable dbt models. Leads complex analyses, dashboards, scenario planning, and executive communication while mentoring analysts and partnering with engineering and strategy teams. Supports AI enablement by creating trusted data models, evaluation standards, and agent performance tracking.

Build and maintain SQL and dbt data models, tables, views, dictionaries, and self-service tools on BigQuery. Support Accounting, Actuarial, and Claims stakeholders from request through production, monitor data quality, prevent defects, document processes, and review AI-generated code. The role requires hands-on data development, collaboration across projects, and growing ownership of a data domain and stakeholder relationships.

Develop resource allocation, demand forecasting, and capacity models for Customer Success. Analyze quantitative and qualitative data, identify trends and risks, and translate findings into actionable recommendations. Partner with finance, planning, delivery, and leadership stakeholders to refine assumptions, resolve complex business issues, and support regional implementation. Present insights to senior leaders and manage cross-functional priorities in an ambiguous, fast-paced environment.

Build and maintain dbt transformation models and Power BI semantic layers on Microsoft Fabric. Establish data quality testing, observability, governance, CI/CD, documentation, and access controls. Partner with business and technology stakeholders to define reliable metrics and data products, optimize performance and costs, troubleshoot issues, and mentor analysts and engineers while advancing analytics engineering standards.

Analyzes claims data to identify trends, anomalies, root causes, cost drivers, and operational inefficiencies. Partners with claims, quality assurance, SIU, finance, and leadership teams to define analytical requirements, develop recommendations, improve claims handling, reduce risk, and enhance efficiency. Presents actionable findings to technical and non-technical stakeholders, including adjusters, managers, and executives.