Top Data & Analytics Jobs in Ann Arbor, MI

Design and implement attribution, source tracking, CRM integrations, ROI measurement, funnel reporting, and sales performance systems for B2B clients. Consult with revenue leaders, define metrics, build and validate reporting across marketing, sales, CRM, advertising, and BI platforms, troubleshoot data quality issues, document solutions, and train client teams. Contribute to implementation methodologies, technical content, automation, and AI-enabled data validation and analysis.

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.

Reviews and annotates video content by identifying objects, people, actions, events, and visual details for AI training. Follows project guidelines, checks annotation quality, corrects inconsistencies, flags unclear content, meets productivity targets, and adapts to updated requirements. The role requires independent, detail-focused work, strong written English comprehension, basic computer skills, reliable internet, and personally provided equipment suitable for video annotation.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Designs, develops, tests, and maintains scalable public health data pipelines, enterprise data marts, and cloud-based informatics systems. Uses Databricks, SQL, Python, PySpark, and related technologies to integrate, transform, curate, and govern multisource data. Performs data quality assurance, monitoring, troubleshooting, documentation, and dashboard development while collaborating with clinical, technical, public health, and external partners to improve interoperability, surveillance systems, and data-driven decision-making.

Develop and maintain scalable data pipelines, ETL/ELT processes, APIs, data models, and analytics datasets using Python, SQL, Spark, and Databricks. Integrate sensitive federal health, financial, and operational data while implementing validation, governance, testing, documentation, and performance optimization. Collaborate with engineers, architects, analysts, and stakeholders to deliver reliable data products and resolve technical data needs.

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.

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.

Investigate and resolve data integration, file processing, and transfer issues. Monitor automated jobs, use SQL and SSIS to validate data, manage SFTP workflows, reprocess files, document integration processes, and collaborate with internal teams to ensure accurate and reliable client and vendor data exchanges.