Top Analysis Reporting Jobs in Detroit, MI

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

Leads scalable data model and pipeline design, dbt development, testing, CI/CD, and analytics engineering implementations. Partners with analysts, engineers, and stakeholders to translate business needs into reliable models and semantic layers. Advises on data architecture, governance, quality, performance, and long-term scalability while mentoring peers and contributing to shared tooling, standards, and frameworks.

Staff Data Engineer responsible for defining data architecture strategy, building fault-tolerant batch and real-time streaming infrastructure, and managing scalable lakehouse and database systems. The role processes telemetry from millions of connected devices, drives performance tuning and data quality, supports on-call operations, evaluates modern technologies, and mentors engineers. It requires strong expertise in Python, SQL, cloud data platforms, streaming systems, infrastructure as code, and enterprise-scale architecture.

Build and maintain finance data pipelines, warehouse models, dashboards, and automations for unit economics, technology budgeting, burn, runway, margins, deal desk, and sales compensation. Develop internal tools and LLM-based agents that help non-engineers access trusted financial data. Partner with finance, sales, and engineering leadership to reconcile discrepancies and improve revenue, cost, billing, payroll, cloud-spend, and accounting workflows.

Build and manage scalable ETL/ELT pipelines using Microsoft Fabric and Azure Databricks, implement medallion architecture, support data migrations and legacy SSRS decommissioning, develop Power BI data models, and maintain governance, security, and CI/CD practices. The role also supports migration events and modernizes data infrastructure for advanced analytics and reporting.

Analyze contributor performance, dataset quality, production efficiency, and client delivery metrics. Build dashboards and reports, conduct cohort and trend analysis, monitor quality benchmarks, identify data integrity issues, and translate findings into actionable recommendations. Collaborate with Data Operations, Engineering, Product, and Quality Assurance teams to define metrics and evaluate improvements.

Designs and maintains scalable data architecture, warehouses, ETL/ELT pipelines, and data platforms. Responsibilities include ensuring data quality, integrity, governance, accessibility, reliability, and performance; supporting customer onboarding and analytics tools; contributing to architecture and engineering best practices; troubleshooting complex data issues; and participating in on-call rotations. The role collaborates with engineering, product, solutions, business intelligence, and other cross-functional teams in healthcare and social care data environments.

Leads enterprise analytics strategy, measurement frameworks, KPI development, dashboards, reporting, and insights for B2B commerce, marketing, and customer experience clients. Oversees analytics implementations across web, commerce, CRM, CDP, BI, and data platforms; mentors analysts and consultants; manages data quality and governance; leads executive client discussions, workshops, proposals, and business growth opportunities.