Top Data & Analytics Jobs in Detroit, MI

Leads end-to-end healthcare payer data conversions between platforms, including profiling, transformation mapping, ETL design, mock and parallel runs, bulk loading, validation, testing, defect resolution, documentation, and client reporting. Develops Python and SQL conversion routines, manages risks and timelines, collaborates with onshore and offshore teams, and mentors junior staff. Requires expertise in claims, enrollment, or provider data and extensive technology integration leadership.

Build and maintain scalable ETL/ELT pipelines, integrate travel and customer data from internal and external systems, develop cloud data warehouse models, implement data quality monitoring, support real-time streaming, and ensure data privacy and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, pricing, route optimization, and customer experience initiatives.

Develop and deploy business intelligence applications for a U.S. Air Force financial platform across development, testing, integration, and production environments. Design and optimize Informatica ETL pipelines, Oracle databases, SQL and PL/SQL processes, data warehouses, BI semantic layers, and SAP integrations. Support Agile delivery, budgeting integrations, monitoring, interface changes, and security hardening. The role also requires Oracle APEX, WebLogic, HTTP Server, performance tuning, and collaboration on data-related issues.

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.

Conduct quantitative and qualitative research, analyze complex business scenarios, gather requirements, and synthesize stakeholder perspectives into actionable recommendations. Evaluate data using tools such as Excel or SQL, identify process improvements, and produce clear reports, memos, business cases, and documentation. Collaborate with stakeholders through written communication and contribute analytical content to help train AI systems.

Reviews, develops, validates, and refines dashboards, reports, visualizations, calculations, data sources, KPIs, and analytical workflows across BI platforms. The role documents findings, provides structured feedback, troubleshoots reporting accuracy, and evaluates AI-powered agents and workplace connectors used in analytics processes.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

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.