Top Analysis Reporting Jobs in Detroit, MI

Designs, develops, and maintains data platforms, pipelines, and integration solutions across on-premises and cloud environments. Responsibilities include resolving data quality and security issues, building ELT processes, developing data scripts and relational or non-relational models, and monitoring data lake performance and scalability. The role also supports analytical and operational decision-making through reliable data solutions and collaborates with cross-functional teams.

Develop and optimize validation KPI dashboards, Jira workflows, and Google Sheets tools. Use Python, Power BI, Databricks, and automation to analyze validation data, identify risks, and improve reporting. Create validation processes, documentation, training materials, and standard work while supporting Lean Six Sigma continuous improvement and change management. Maintain data repositories and help validation teams track readiness, accountability, and completion milestones.

Analyze complex datasets, develop data models, dashboards, and visualizations, and conduct predictive and statistical analysis for client decision-making. Collaborate with clients to deliver tailored analytics solutions, maintain data integrity, communicate insights through reports and presentations, and support data-driven operational strategies using SAS, Power BI, Tableau, and SQL tools.

Analyze warranty and reliability data to identify trends, risks, and cost drivers; develop dashboards and reports; support root-cause analysis, cost forecasting, and customer negotiations; ensure data integrity and drive continuous improvement across cross-functional teams.

Lead regional economic intelligence and outreach, conduct qualitative and quantitative research, produce sector and district analysis for policy use, prepare publications and presentations, engage senior external stakeholders, and mentor junior economists.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Own GTM performance analytics, KPI and target setting, reporting, forecasting, compensation planning, and performance reviews across Sales and Post-Sales. Identify anomalies and optimization opportunities involving conversion, churn, productivity, pricing, packaging, segmentation, channel economics, and customer quality. Partner with Finance and GTM leaders to develop recommendations, lead strategic initiatives, and ensure data integrity while translating analysis into actionable business decisions.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

Build and operate production-grade ELT pipelines from product, Salesforce, and third-party systems into Snowflake using dbt. Ensure testing, monitoring, schema versioning, governance, privacy, access controls, data quality, cost, and performance. Support AI initiatives with trusted data, contribute to warehouse and semantic-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.