Top Data & Analytics Jobs in Detroit, MI

Design and own a unified data model for an AI-driven pipeline risk platform. Responsibilities include lakehouse and medallion architecture, geospatial modeling, entity resolution, ingestion patterns, governance, lineage, data quality, access controls, and traceability. Partner with data engineers, application engineers, and data scientists to implement schemas, pipelines, serving layers, and optimization strategies while maintaining clear architecture documentation.

Supports cross-functional manufacturing, finance, engineering, IT, and operations initiatives through data consolidation, business analysis, KPI reporting, requirements gathering, and cost-benefit analysis. The role develops recommendations, project status reports, presentations, and post-project audits while helping support strategic decisions and operating effectiveness. This position is remote within specified states and requires up to 25% travel.

Leads the data engineering organization and enterprise Databricks Lakehouse on AWS. Owns team development, platform strategy, delivery, reliability, governance, budget, vendors, architecture standards, and regulatory compliance. Partners with executives on data transformation, MDM, ERP migration, analytics, and AI enablement. Requires extensive data engineering leadership, Databricks and AWS expertise, regulated-environment experience, and proven budget and vendor management.

Analyze quantitative and qualitative customer data using Adobe Customer Journey Analytics and BI tools. Build data pipelines, segments, calculated metrics, dashboards, reports, and executive presentations. Translate findings into actionable business recommendations, validate deliverables, support client decision-making, and improve analytics processes. The role also requires managing project plans, collaborating across teams, presenting insights, and debugging webpages to verify analytics data collection.

Manage Oracle and PostgreSQL databases across on-premises and AWS environments, including upgrades, migrations, performance tuning, security, and backup recovery. Design and maintain ETL pipelines using Talend, SQL, Power Automate, and related tools. Perform data profiling, quality analysis, governance, and cloud migration activities. Create technical architecture and lineage documentation while supporting regulated, audit-ready data platforms and integrations.

Lead the architecture, design, development, troubleshooting, and optimization of Snowflake data solutions for higher education clients. Conduct discovery and design workshops, assess existing environments, create enterprise data models and future-state architectures, develop ETL/ELT pipelines, implement security and data-quality controls, and optimize performance and costs. Serve as a client-facing technical advisor, translate business requirements into scalable solutions, lead technical workstreams, support business development, mentor teams, and travel periodically.

Own customer data migration projects from analysis and cleanup through execution, validation, and communication of results. Develop and maintain migration scripts and tooling, work with APIs and structured data, resolve quality issues, and collaborate with Customer Success, Support, Product, and Engineering. Manage multiple projects, handle complex datasets, answer customer questions, safeguard sensitive information, and improve migration processes, documentation, and scalability.

Leads analytics engineering and analytics-ready data architecture for government clients. Designs standardized data models, transformation logic, validation frameworks, and reusable integration patterns. Governs data quality, QA, backfills, and release processes while resolving complex data issues. Integrates web, email, social, survey, and client-system data to support dashboards, reporting, measurement, and insights. Advises stakeholders, influences enterprise standards, coordinates with product teams, and supports client-facing analytics workstreams.

Own measurement strategies for large omnichannel brands, including test design, marketing mix modeling, incrementality analysis, attribution interpretation, and budget recommendations. Translate complex findings into actionable guidance for executives and marketing teams, support prospect conversations, identify customer and product gaps, and collaborate with Product, Data Science, and Customer Success. The role requires strong paid media expertise, omnichannel fluency, statistical measurement literacy, and the ability to communicate uncertainty and recommendations clearly.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.