Top Analysis Reporting Jobs in Detroit, MI

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

Own and evolve the analytics engineering platform: design and maintain certified dbt models, enforce modeling/testing/documentation standards, build semantic and metadata layers, optimize Snowflake performance, partner with analysts and data engineering to migrate business logic and ensure reliable, AI-ready analytics, and mentor teammates on analytics engineering best practices.

Own RevOps data models and BI infrastructure to drive GTM strategy. Clean and analyze datasets, build predictive models, define and track leading/lagging KPIs, deploy dashboards, document data definitions, and deliver executive-ready insights while partnering cross-functionally with Finance and GTM teams.

Join Kled to help build the future of human data collection and design systems that influence AI data training through a consumer application.

Own and build the support reporting layer: ticket volume, SLA, resolution times, CSAT/CES/sentiment, and capacity. Produce dashboards and analyses for leadership and engineering, maintain data quality and governance, prioritize issues using multi-signal models, and surface Voice of Customer trends to close the loop with Product and GTM.

Lead end-to-end encounter data management for CMS/state Medicaid submissions. Validate and optimize ETL converting source data into HIPAA X12 837P, support regulatory audits, analyze trends and root causes, liaise across business/technical/operations teams, and recommend process improvements to ensure compliance and data integrity.

Develop and maintain datasets for visualization and machine learning; automate data pipelines; partner with teams to build data products.

Lead development of scalable marketing data models, pipelines, and BI solutions. Partner with Marketing to define metrics, build dashboards, enable self-service analytics, and drive data governance and best practices.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Architect, build, and maintain Snowflake-based ELT pipelines and production data models. Implement observability, data quality, and governance; optimize cost and freshness. Partner with Analytics, Product, and Engineering, use dbt/CI-CD and AI-assisted tooling, and participate in reviews and architecture planning to support analytics, BI, and ML.