Top Analytics Jobs in Minneapolis, MN

The Senior Data Analytics Engineer will develop data pipelines, support data-driven decision-making, collaborate with stakeholders, and improve data quality and processes.

The Data Scientist will enhance statistical models for forecasting, analyze datasets for operational planning, and communicate insights to stakeholders.

The Senior Analytics Engineer will design and build scalable data pipelines using dbt and Snowflake, providing technical guidance, conducting audits, and mentoring junior engineers while leveraging AI tools to enhance productivity.

Lead analytics for Omnichannel retail/restaurant/CPG partners: act as primary analytics contact for Sales, manage analytic engagements and team capacity, perform SQL-based analysis, craft data-driven narratives and recommendations, influence campaign strategy, represent analytics in cross-functional settings, and automate insights using AI-enabled tools.

The Analyst will analyze data, develop programming for data extraction, create reports, investigate trends, and monitor statistical models to enhance business decisions.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Embed on the Video product team to build reusable data models, transformation layers, and pipelines (Snowflake, dbt/SQLMesh). Deliver production-ready data products (APIs, dashboards), instrumentation, experimentation infrastructure, and predictive/causal models to inform product, retention, pricing, and growth decisions.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

As a Senior Analyst, you will develop data-driven solutions in collaboration with operations teams and deliver insights through reports and dashboards to improve patient outcomes.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Lead a small data engineering team partnering with Engagement Marketing to build scalable ETL/ELT pipelines, ensure data quality, define marketing metrics, integrate trusted data with ad platforms, and drive the data roadmap to enable measurement and growth.

Lead a team supporting Epic analytics and clinical/operational systems. Oversee daily operations, project coordination, testing, reporting, data governance, staffing, training, and vendor interactions. Design scalable data models and pipelines (Microsoft Fabric, SQL Server, Power BI), ensure data quality, and present findings to executive stakeholders while maintaining system availability and compliance.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

Lead and grow a BI and analytics team to build scalable, AI-enabled reporting and self-service analytics. Partner with cross-functional stakeholders to deliver executive dashboards, standardize metrics, ensure data quality and governance, and use AI tools to automate insights and accelerate dashboard development.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Design, build, and own Sardine's internal data platform and billing infrastructure. Implement and maintain ETL/ELT pipelines, warehouse partitioning, access controls, and data governance. Partner with stakeholders to define KPIs, produce dashboards, and ensure data quality for executive, GTM, product, and finance teams.

The Data and Analytics Consultant will design, develop, and implement data reporting, visualizations, and analytics solutions for clients while assisting them in data understanding and value extraction.

The Senior Manager of Risk Analytics will lead credit risk strategies, manage portfolio performance, optimize decisioning frameworks, and drive business growth through analytics and predictive modeling.

The Senior BI Analytics Engineer will design and deliver data models for analytics, ensuring data quality, and collaborating across teams to enhance AI-driven reporting and querying capabilities.

The Analytics Architect will design and implement cloud-based analytics solutions, maintain data pipelines, ensure data quality, and collaborate with teams to enhance operational efficiency.

Design and maintain scalable dbt data models and Looker dashboards; partner with Finance, Product, and GTM to translate requirements into analytics solutions; ensure data quality with Data Engineering; perform analyses, forecasting, and QA to deliver trusted reporting and drive analytics initiatives.

Build and maintain dbt data models, design BI dashboards and reports (Count and other tools), and deliver merchant/issuer analytics. Partner with GTM and product teams to translate data into actionable insights, improve data quality, and lead technical investigations to scale analytics infrastructure.

Lead full-stack analytics at Air by designing and maintaining dbt models and BigQuery transformation layer, ensuring data quality and observability, owning ingestion/orchestration, building self-serve BI (Mode/Hex) and AI-native interfaces, and partnering with product, growth, and GTM teams to deliver trusted data products and strategic insights.