Top Senior Analysis Reporting Jobs in Detroit, MI

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Own product, content, subscription, and growth analytics for adult product lines. Analyze conversion, retention, churn, cohorts, and LTV; maintain reporting; create measurement plans; support experimentation; and deliver recommendations to Product, Engineering, Content, and Design. Use SQL, visualization tools, statistical methods, and AI-assisted workflows while validating results and documenting work.

Designs, builds, and maintains Databricks data pipelines and analytics solutions using SQL and Python. Collaborates with development and IT teams, applies DevOps and CI/CD practices, supports Power BI dashboards, and contributes to data modeling, management, and governance. The role requires extensive Databricks and cloud-platform experience, strong communication skills, fluent English, and at least five years of relevant experience.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and high-quality data products. Design scalable processing and storage patterns, orchestration workflows, and data warehousing solutions while collaborating remotely and documenting technical designs.

Leads customer analytics, revenue and retention reporting, executive and board reporting, renewal variance analysis, capacity modeling, customer health scoring, churn prediction, and data governance. Partners with Finance, Sales, Revenue Operations, Product, and Customer leadership to standardize SaaS metrics, improve retention, forecast revenue, and translate complex datasets into strategic recommendations and executive-ready dashboards.

Modernize an Azure Synapse platform and migrate workloads to Microsoft Fabric. Build metadata-driven ingestion frameworks, Fabric pipelines, Spark notebooks, validation and reconciliation processes, and Git-based CI/CD workflows. Analyze complex Synapse dependencies, implement full-load, incremental, and CDC patterns, and support medallion architecture, governance, lineage, monitoring, and security. Collaborate with client engineers through paired development, architecture discussions, code reviews, and knowledge sharing while using Claude Code for AI-assisted development.

Investigate fraudulent accounts, payouts, traffic sources, and partner activity across rewarded mobile apps. Analyze fraud patterns and MMP data, build risk rules and verification controls, quantify financial impact, and report results. Partner with Product, Engineering, Growth, Support, and external partners to prevent fraud while minimizing false positives. The role also establishes review standards, mentors teammates, evaluates fraud tools, handles appeals, and helps develop AI-assisted fraud workflows.

Analyzes and processes large-scale assessment datasets, maintains psychometric software and statistical code, troubleshoots programs, develops quality controls, coordinates data transfers, supports standard-setting studies, and communicates results. The role also improves workflows, mentors colleagues, supports project planning, and collaborates with psychometricians and technical teams.

Build and maintain scalable, fault-tolerant data pipelines, warehouses, and analytics solutions. Transform complex structured and semi-structured data, automate delivery processes, improve data quality and reliability, and support enterprise data strategies. Collaborate with analysts, data scientists, actuaries, and business teams to translate requirements into solutions. Research new technologies, troubleshoot issues, maintain version-controlled code, and mentor data engineering team members.