Top Data & Analytics Jobs in Detroit, MI

Develop and support enterprise business intelligence solutions by aggregating data, analyzing data sources, and building Tableau dashboards, reports, and SQL queries. Partner with stakeholders to translate business needs into technical solutions, document data domains, support data modeling and governance, and collaborate with Reporting, Analytics, and Data Engineering teams. The role requires healthcare data warehousing experience, strong SQL Server BI skills, stakeholder support, and compliance with privacy and security requirements.

Build scalable data and AI platforms, APIs, intelligent agents, and cloud-native applications using generative AI and LLMs. Lead architecture and technical strategy, create reusable AI frameworks and services, establish governance and security practices, and provide hands-on engineering leadership through coding, reviews, mentoring, and cross-functional collaboration.

Maintains accurate, consistent data across Microsoft Dynamics and other company systems. Performs data entry, QA/QC audits, discrepancy resolution, duplicate record cleanup, data governance, documentation, and process improvement. Serves as the organization-wide contact for data entry needs and collaborates with multiple departments to establish standards, improve workflows, and support reliable reporting and operations.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Supports Texas Workforce Commission technology initiatives through business and systems analysis. Documents functional, technical, accessibility, and security requirements; develops user stories, process maps, conceptual and object models, use cases, and architecture diagrams. Creates and executes test cases, ensures requirements traceability, supports legacy-system interfaces, facilitates cross-functional workshops, and assists with project lifecycle artifacts.

The Analytics Engineer develops end-to-end analytics solutions and business applications to streamline Operations and enable digital transformation. Responsibilities include communicating analytics to nontechnical stakeholders, supporting cross-functional supply chain, manufacturing, and quality projects, developing business processes, selecting methodologies and technologies, and improving operational efficiency. The role is fully remote and may involve AI technologies, full-stack development, and optimization modeling.

Develops scalable data models, Databricks pipelines, backend applications, ETL processes, and data ingestion systems. Partners with engineering, product, and business stakeholders on technical roadmaps; monitors and optimizes data systems for performance, reliability, and scalability. Provides technical leadership, mentors junior engineers, conducts code reviews, and builds secure solutions for sensitive healthcare data.

Designs, builds, and maintains scalable data pipelines, data models, and platform infrastructure supporting enterprise reporting, analytics, and AI-driven decisions. Responsibilities include ingesting SaaS application data, developing medallion architecture layers and dbt transformations, implementing data quality monitoring, enforcing governance and access controls, and delivering reusable semantic data assets. The role collaborates with analytics, BI, and business teams, participates in Agile delivery, evaluates modern data-stack tools, and promotes engineering excellence through testing, documentation, and code reviews.

Design, build, and operate scalable cloud-native data pipelines and data lake/fabric architectures for Revenue Cycle Management. Implement ETL/ELT, integrate structured and unstructured sources and APIs, ensure data quality, lineage, and governance, and support AI/ML and analytics workloads with CI/CD and DevOps practices.

Lead and own end-to-end delivery of enterprise Data & AI programs, define data strategy and architecture (lakehouse/warehouse), drive modernization, governance, data quality, metadata/lineage, and AI adoption. Manage client relationships, account growth, program budgets, delivery teams, and stakeholder communications to ensure on-time, on-budget outcomes.