Top Remote Data & Analytics Jobs in Ann Arbor, MI

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Build customer health scores, churn and expansion analytics, retention dashboards, automated alerts, and executive reports. Maintain integrations among databases, CRM, and BI tools while improving data quality and standardizing reporting. Lead AI adoption for analytics, validate generated insights, and enable customer-facing teams to make informed retention and expansion decisions. Requires strong BI, logistics or supply chain analytics, dashboarding, data integration, communication, and customer-focused skills.

Analyze market, industry, competitor, and customer intelligence for Vistex’s served industries. Assess market opportunities, monitor competitors, identify trends, develop competitor profiles, and translate complex information into actionable insights. Findings support go-to-market strategies, product roadmaps, marketing direction, competitive positioning, pipeline development, and revenue growth. The role collaborates with industry, sales, marketing, product, and pre-sales stakeholders while managing multiple projects.

Develop and maintain data pipelines, infrastructure, and integrations supporting analytics, data science, and business intelligence. Ensure data quality and integrity, optimize workflows, and collaborate with Operations, IT, Accounting, HR, Property Services, Marketing, and other departments. The role requires SQL Server, programming, shell scripting, cloud, containerization, version control, and data pipeline knowledge, with occasional travel and flexible scheduling as project deadlines require.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Develop customer analytics that improve retention, growth, adoption, and operational performance. Build and maintain BI dashboards, customer health metrics, churn-risk reporting, and predictive models. Partner with Client Success, Product, Sales, and Operations to translate complex findings into actionable recommendations. The role also owns data quality, governance, reporting automation, workflow improvements, and stakeholder communication.

Develop and support enterprise BI solutions, including dashboards, reports, governed datasets, data marts, and ETL/ELT processes. Translate business requirements into technical designs, perform data analysis and validation, troubleshoot production issues, and maintain documentation, governance, security, and data-quality standards. Lead-level candidates will guide technical decisions, mentor team members, and coordinate complex initiatives and incidents.

Owns and maintains organizing data systems, voter-contact tools, and organizational databases. Ensures data integrity through quality assurance, audits, standardized processes, documentation, and permission management. Supports organizers by troubleshooting tools, managing data requests, providing training, and improving workflows. Writes SQL queries in BigQuery, develops reports and visualizations, supports integrations, and collaborates with engineering and analytics teams on data models and priorities.

Own the analytics engineering roadmap, architecture, and production data models. Build and optimize dbt models using SQL, Python, and Databricks; establish dimensional models, governed metrics, testing, documentation, lineage, monitoring, and CI/CD practices. Partner with data engineering, analysts, BI developers, and stakeholders to deliver reliable analytics products, improve performance and cost efficiency, investigate data quality issues, and mentor team members.

Organise and curate large amounts of raw material: extract key points from transcripts, collate and maintain clean spreadsheets, tag and categorise links and documents, and create findable information structures following or suggesting systems.