Top Analysis Reporting Jobs in Detroit, MI

Analyze and model property catastrophe reinsurance treaties using catastrophe modeling platforms. Prepare and validate exposure data, execute models, interpret risk metrics, develop scenarios, and identify portfolio risk drivers. Support underwriting, actuarial pricing, placement, renewals, and management decisions through clear reporting. Participate in model validation, calibration, research, and development while collaborating with underwriters, actuaries, and other stakeholders.

Owns and maintains organizing data systems, voter-contact tools, and organizational databases. Ensures data integrity through quality assurance, audits, standardized processes, documentation, and permission management. Supports organizers by troubleshooting tools, managing data requests, providing training, and improving workflows. Writes SQL queries in BigQuery, develops reports and visualizations, supports integrations, and collaborates with engineering and analytics teams on data models and priorities.

Organise and curate large amounts of raw material: extract key points from transcripts, collate and maintain clean spreadsheets, tag and categorise links and documents, and create findable information structures following or suggesting systems.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.

Build and maintain marketing data pipelines connecting Salesforce, Marketo, and cloud data warehouses. Automate campaign reporting, develop SQL datasets and views, improve attribution and data quality, support CRM and marketing automation configuration, and document data models and processes. Use AI tools such as Claude to accelerate analytics workflows while ensuring reporting accuracy. Partner with Marketing Analytics, Demand Generation, and Marketing Operations to support executive-level pipeline reporting.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Lead GTM analytics across performance measurement, channel economics, data governance, forecasting, planning, and executive reporting. Own metric definitions, data standards, analytical priorities, and business requirements while partnering with Marketing, Data Science, Finance, RevOps, and Engineering. Analyze funnel, attribution, CAC, payback, conversion, retention, pipeline, and territory performance, translating findings into investment recommendations and strategic decisions for senior leadership.

Contract Analytics Engineer maintaining Looker and LookML dashboards, reporting, data operations, sales pacing, data QA, vendor workflows, board reporting, forecasting, and planning support during a director’s maternity leave. The role requires strong SQL and Snowflake skills, analytics engineering experience, vendor management, Git/GitHub proficiency, documentation, and independent work in a lean data team.

Supports provider business development through bundled-payment pricing analytics, financial modeling, and strategic analysis. Builds and refines analytical tools and datasets, develops models for new healthcare bundles, supports provider negotiations, and partners with data science and engineering teams on optimization and data infrastructure. Requires healthcare knowledge, quantitative skills, cross-functional collaboration, and the ability to manage multiple priorities in a fast-paced remote startup environment.