Top Analysis Reporting Jobs in Detroit, MI

Leads complex, enterprise-scale data engineering initiatives using Databricks and Azure. Designs and implements scalable lakehouse architectures, high-performance pipelines, and platform capabilities while establishing engineering standards for reliability, security, observability, and cost efficiency. Mentors engineers, leads through influence, embeds across teams to accelerate delivery, manages technical risks, and advises stakeholders on data strategies and architectural trade-offs. Collaborates with Architecture, Platform, AI, Analytics, Security, and Governance teams.

Design, operate, and scale end-to-end data infrastructure and pipelines. Build reusable data models and a semantic layer, ensure data quality and privacy, implement observability and SLAs, support marketing and growth feeds, partner with cross-functional stakeholders, and enable ML/AI consumption and reverse ETL workflows.

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Build and maintain scalable data pipelines, warehouse models, metrics, and data quality systems for Airbnb’s AirCover programs. Reconcile external partner data, publish analytics-ready datasets, improve pipeline observability and reliability, and collaborate with engineering, product, finance, operations, analytics, and data science stakeholders. Contribute to data engineering standards and foundational systems supporting reporting, experimentation, claims operations, financial reconciliation, and expansion into new markets.

Support an insurance actuarial department by cataloging and classifying data, maintaining master data mappings and schemas, auditing data quality, enforcing governance policies, and ensuring regulatory compliance. Collaborate with technical and business stakeholders, conduct Alation and Excel training, and lead data governance projects aligned with organizational objectives.

Lead and manage a remote Data Engineering team to build scalable data pipelines and lakehouse solutions. Own execution of Medallion architecture using Databricks, Boomi/Rivery, dbt, Delta Lake and SQL Server; implement DataOps, CI/CD, observability, and stakeholder intake while growing engineers and improving team maturity.

Manage transportation data integrity from intake through ETL and business intelligence platforms. Review and correct data errors, resolve quality and flow issues, analyze large transportation datasets, and create KPI reporting solutions. Deliver client business reviews covering spend, trends, savings, and market impacts. Serve as a client contact while collaborating with audit, finance, development, and analytics teams to improve reporting tools, software, and customer outcomes.

Produces publishable maps, graphs, and visualizations of conflict patterns and analysis outputs. Transforms complex datasets into compelling visual stories, develops visualization guidelines, researches new methods and software, and supports visual brand development. Collaborates with regional experts, media, partners, and external stakeholders, responds to journalist inquiries, ensures data accuracy, and contributes to outreach, reporting, coordination, and communications activities.

Provide insights and recommendations to improve end-to-end purchase customer journeys across web and app for B2C and B2B. Lead A/B testing and experimentation, ensure accurate data collection and impact measurement, build self-service reporting and visualizations, query and process data, and present findings to stakeholders and senior leadership.

Own high-impact data products, ingestion pipelines, and granular data feeds end to end. Analyze massive datasets to answer client and stakeholder questions, ensure data accuracy, and derive actionable insights. Improve products and pipelines by developing methodologies, scaling third-party datasets, and creating data modules. The role requires clear communication, attention to detail, methodical problem solving, prioritization, experimentation, and strong ownership. Training includes SQL, PySpark, Python fundamentals, and internal tools.