Top Senior Analysis Reporting Jobs in Ann Arbor, MI

The Principal Data Engineer will lead technical direction, manage data platforms, make architectural decisions, mentor engineers, and ensure data systems operate reliably.

Design and build BI solutions and dashboards to surface KPIs and drive decisions. Partner with stakeholders to define metrics, extract and model data (SQL/Python), automate reporting, validate data quality, and provide ad-hoc analysis and predictive insights to leadership.

Design and implement OBIEE solutions and enterprise dashboards for capital markets. Develop RPD models and presentation layers, apply dimensional modeling and data warehouse best practices, enforce OBIEE security/roles, produce technical specifications, prioritize tasks, and liaise with clients and IT teams to gather requirements and deliver analytics solutions.

Design, architect, and implement end-to-end data ingestion, storage, modeling, and provisioning solutions on the GE Predix platform. Lead cross-functional teams, set data architecture standards, develop OLTP/OLAP models, perform POCs, and enable analytics at scale while enforcing data management policies.

Design and own end-to-end data domains and pipelines for plant sensors, lab results, and historians. Build schemas, ensure data quality, observability, and lineage, and supply model-ready time-series and analytical datasets to ML and operations teams. Mentor junior engineers and define data contracts.

Lead development and operation of the enterprise Data & Analytics platform. Own roadmap, platform capabilities, CI/CD, automated provisioning, monitoring, governance, cost optimization, and incident management. Partner with engineering, architecture, security, and business teams, act as product owner for platform services, and manage a team of platform engineers to enable self-service, secure, and scalable data delivery.

Design, build, and maintain Power BI dashboards and data models using Snowflake and other cloud sources. Collaborate with data engineers/ETL teams, gather stakeholder requirements, implement DAX and Power Query transformations, ensure data governance and performance, and deliver analytics within Agile processes.

Design and build petabyte-scale multimodal data pipelines and infrastructure (text, audio, images, video). Own and maintain systems like Spark, Ray, and vector databases, partner with pre/post-training teams to improve data recipes through experimentation, and support large-scale dataset storage and processing for AI model training.

Lead post-sales and revenue analytics by integrating product telemetry, support signals, and revenue data. Build scalable data models, reusable datasets, and semantic layers. Define SaaS metrics, forecast revenue and usage, predict expansion and churn, and deliver dashboards and insights to drive renewals, expansion, and revenue growth.

Lead enterprise reporting strategy and Sigma BI platform administration. Partner with business and data teams to define KPIs, build scalable Sigma data models and dashboards, govern reporting, enable self-service, and integrate Sigma with Databricks, dbt and AI tools to improve analytics quality and adoption.