Top Senior Data & Analytics Jobs in Detroit, MI

Perform customer analytics and lifecycle measurement for subscription and auto insurance products. Build dbt/Snowflake data models, Tableau dashboards, survival and churn analyses, propensity datasets, A/B tests, pricing and LTV analyses, and recommendation logic. Partner with data engineers, data scientists, product, and marketing to operationalize personalization and inform retention, pricing, and product strategies.

The Databricks Architect leads data engineering solutions, designing scalable Databricks platforms and managing cloud implementations. They work with clients, ensuring technical alignment and project execution.

Administer and maintain CRM and data systems (EveryAction, Salesforce, BigQuery, CARTO), respond to staff and affiliate data/tech requests, ensure data quality and de-duplication, create and deliver training materials, build network capacity, manage vendor/consultant relationships, and travel occasionally for in-person trainings and support.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Manage end-to-end analytics for performance guarantees across strategic accounts: execute recovery and accrual analytics, build SQL-based pipelines and Looker dashboards, support RFPs/renewals with scenario modeling, partner with Sales/Customer Success and Data Engineering, and present technical findings to internal leaders and clients to mitigate financial risk and drive actionable insights.

Use statistical modeling and data analysis to generate actionable insights that improve loan origination and production. Collaborate cross-functionally to design, test, and implement solutions, build dashboards and visualizations, maintain data integrity, and communicate findings to stakeholders to inform business strategy.

Build and own the quality analytics data layer: design and maintain dbt models, data marts, and Python-driven analyses. Define and standardize quality metrics, run experiments and A/B tests, support decision-making with dashboards and reports, automate recurring reporting, and partner with Quality and Engineering to ensure reliable, documented analytics.