Top Data & Analytics Jobs in Detroit, MI

Lead credit underwriting, pricing, verification, and portfolio performance for personal loans. Use data and ML/AI to design automated decisioning, manage credit/fraud risk, forecast losses, and present strategies to leadership and investors. Collaborate cross-functionally and build vendor/alternative-data strategies to drive profitable growth while keeping losses within risk appetite.

Design, implement, and optimize Snowflake data storage and ETL solutions. Write and tune SQL, integrate data sources and BI tools, ensure data security and compliance, and collaborate with teams to meet business objectives.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

The SAP IBP Analyst will optimize supply chain planning processes in e-commerce, focusing on SAP IBP modules, system integrations, and stakeholder management.

Lead a small analytics team to produce data-driven insights for marketing and media optimization. Mine proprietary datasets, apply statistical methods, forecast KPI impacts, monitor business metrics, and act as liaison between business and technical teams to drive profitable media and client growth.

Analyze high-scale payment funnel and transactional data to identify conversion bottlenecks, design and evaluate A/B tests, define metrics and measurement frameworks, monitor product performance via dashboards, and translate insights into prioritized product recommendations in partnership with Product and Engineering.

Define and govern enterprise data architecture, models, canonical and semantic layers, field-level mappings, and data governance. Partner with IT, Data Engineering, Security, and product teams to ensure data quality, lineage, integrations (Boomi), and platform alignment (Salesforce, Snowflake, NetSuite). Lead M&A data activities, perform hands-on data profiling and validation, and drive adoption of enterprise data standards to support analytics, AI, and automation initiatives.

Lead design and implementation of end-to-end ELT pipelines and transformation layers (dbt) to produce curated, reporting-ready datasets. Define data models, governance, metadata, lineage, and engineering standards. Partner with cross-functional teams to translate requirements, optimize performance and costs (cloud DW like Snowflake), and grow a data architecture practice that supports BI, self-service analytics, and AI agents.

Technical leader for Data Engineering responsible for designing, operating, and optimizing Snowflake-based data platforms, self-hosted orchestration (Prefect), dbt practices, AWS infrastructure, and data pipelines. Drive architecture, mentor engineers, partner cross-functionally, ensure observability and production-grade practices, and enable model-ready datasets and AI/LLM workflows.

Lead architecture and operations for the end-to-end data lifecycle: pipeline orchestration, dbt projects, cloud warehouse performance, dimensional modeling, data observability, HIPAA controls, and platform governance. Mentor a distributed data engineering team, partner with Analytics and Finance, and prioritize durable platform capabilities that enable BI, experimentation, and AI use cases.