Top Analysis Reporting Jobs in Detroit, MI

Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.

Own Toast’s Voice of the Customer program across customer segments, analyzing NPS, CSAT, CES, qualitative feedback, and business metrics. Identify root causes of customer pain, develop data-driven recommendations, improve CX measurement practices, and influence cross-functional teams to prioritize customer-focused solutions. The role also builds listening channels, promotes customer-centric decision-making, and aligns CX priorities with organizational goals.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

Leads global contact center capacity planning, forecasting, headcount, and hiring strategies. Builds predictive staffing models using Python, analyzes contact flows and staffing accuracy, and develops reports with MicroStrategy, Alteryx, and Power BI. Communicates data-driven recommendations to executives, partner managers, vendors, and operations teams while managing planning initiatives and optimizing service levels, customer experience, and costs.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.