Top Analytics Jobs in Detroit, MI

Lead development of part content and analytics: build and sustain Power BI dashboards and AI agents, automate processes, manage PIM/content (PartSmart, ERAPA, ARC), perform data audits and storytelling, collaborate cross-functionally to prioritize data, and drive photography strategy (physical and AI rendering).

Design and implement analytic solutions to transform configuration data into actionable insights. Automate data collection from Oracle, Windchill, and TIPQA, build Power BI visualizations, perform statistical analyses, and recommend process improvements using RCCA and Lean while communicating findings to stakeholders.

The Director, Research - Power Markets is responsible for leading the QA/QC process for long-term forecasting, advising leadership, and ensuring market insights are effectively integrated into forecasts.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.

Lead CX analytics strategy and operations by standardizing metrics, enabling self-service analytics, and embedding AI in the analytics lifecycle to enhance decision-making processes.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Use advanced analytics and ML to design and monitor credit risk models, optimize underwriting and credit strategies, partner with Engineering/Product/Compliance, oversee portfolio performance and regulatory alignment, and lead cross-functional initiatives to balance growth, compliance, and risk mitigation.

The analyst will own planning for launches, build forecasting models, partner with stakeholders, and drive data-driven decisions using AI and analytics.

The Analyst I will own analytics workflows, drive improvements, build data models, and support business metrics while collaborating across teams.

The Analyst II will analyze data to optimize fraud strategies, develop machine learning models, and collaborate with teams to enhance product safety.

Designs, builds, and operates ELT data pipelines and analytical datasets for Platform Analytics. Curates and models source data into conformed datasets, ensures performance, reliability, and cost efficiency, contributes to target-state architecture, mentors junior engineers, and uses AI coding tools to accelerate development and delivery.

Lead analytics engineering for Revenue, Marketing, and Finance by building scalable data solutions, data models, and dashboards; provide training and consultation, support ad hoc data needs, and translate technical findings into executive-level insights to improve operational performance.

Develop, validate, and maintain Power BI reports and dashboards using DAX and Power Query. Extract, clean, and analyze data with SQL, Excel, Python and VBA to support supplier quality metrics (PPAP, CRV, Run at Rate, QPR). Gather requirements, enable drill-down reporting, schedule refreshes, deploy reports to production, and act as SME for supplier quality systems (SPPS, SQMS, SCMS). Hybrid role requiring in-office presence three days weekly.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Lead Finance Data & Analytics to build trusted data sources, governance, and analytics for Finance and cross-functional stakeholders. Manage analysts, drive data strategy and Snowflake data models, produce insights via SQL and Tableau, and deploy AI solutions to optimize finance decision-making and operations.

Lead a team of analysts and data scientists embedded in sales to build rigorous pilot analyses, create executive-ready business cases, expand consultative analytics across the partner lifecycle, and publish industry insights while coaching and scaling the Solutions Analytics function.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

As a Forward Deployed Data Analyst, you will configure and implement data systems for clients, ensuring their analytics are accurate and actionable. You'll work with teams to refine metrics and improve data processes, ensuring value from day one.