Top Senior Data & Analytics Jobs in Minneapolis, MN

Designs and leads enterprise data architecture across hybrid cloud and on-premises banking environments. Advises senior leadership, defines target-state architectures, modernizes legacy platforms, establishes standards and reusable patterns, evaluates technology investments, and addresses scalability, security, resiliency, cost, and performance. The role requires hands-on expertise in distributed data systems, integration, analytics, AI/ML workloads, data modeling, and security architecture, along with mentoring and cross-functional influence.

Senior Data Engineer responsible for Snowflake administration, infrastructure and architecture, performance tuning, production support, monitoring, backup and recovery, provisioning, patching, upgrades, capacity management, and incident, problem, and change management. The role also involves automation, DevOps, security and compliance, lifecycle management, on-call operations, documentation, team training, and hands-on Python development for responsible AI-powered solutions.

Leads applied AI and health data architecture initiatives for payer clients. Designs cloud-native platforms, pipelines, data models, integrations, and modernization roadmaps across AWS, Azure, Databricks, and Snowflake. Oversees data quality, governance, anonymization, performance optimization, and legacy migrations while advising stakeholders on AI-ready architectures. Manages multi-team delivery, resolves technical issues, presents solution tradeoffs, and coaches practitioners.

Collects, processes, and analyzes complex datasets to produce actionable insights, automated reports, dashboards, and visualizations. Partners with cross-functional teams to ensure data accuracy, integrity, and effective decision-making. Performs statistical analysis, validates data, improves reporting processes, and coaches stakeholders on data consumption. Preferred experience includes applying AI tools, developing AI-enabled solutions, translating business needs into AI use cases, and following responsible AI practices.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouse architecture. Partner with cross-functional teams to define KPIs, ensure data quality and governance, own billing data infrastructure, and deliver dashboards and analytics for leadership while mentoring peers.

Leads analytics initiatives supporting external health plan partnerships, including provider groups and employer partners. Oversees dashboards, financial, quality, and operational reporting; validates data accuracy; interprets trends; and delivers actionable recommendations. Directs analyst workflows, performs quality assurance, mentors team members, and communicates data-driven insights to external stakeholders. The role requires collaboration across business segments and supports continuous improvement of reporting processes and partner relationships.

Manages enterprise master data governance, quality, standards, controls, and continuous improvement across JD Edwards and related systems. Leads the Master Data team, sets priorities, coaches employees, and ensures accurate, timely data management. Partners with business leaders on system implementations, integrations, reporting, process improvements, and data strategy. Develops scorecards, documentation, training, and corrective actions while resolving complex data and stakeholder issues.

Leads architecture and development of QBP’s modern data platform, including ETL/ELT pipelines, Microsoft Fabric and OneLake lakehouse modernization, enterprise-system integrations, dimensional modeling, governance, and production reliability. Develops machine learning models, AI agents, and MLOps workflows while evaluating emerging technologies. Provides technical leadership, mentorship, incident response, and cross-functional collaboration across BI, SAP, WMS, eCommerce, security, and infrastructure teams.

Own and evolve the Azure data platform for a high-volume telematics SaaS product. Design Cosmos DB, Azure Data Explorer, Azure SQL, and Fabric architectures; build IoT ingestion, ETL, transformation, and analytics pipelines; optimize data models, reliability, observability, governance, and costs; and support production operations. Collaborate with architects, backend engineers, product leaders, and project managers on scalable, multi-tenant data solutions.

Leads the design, development, migration, and deployment of scalable data pipelines and cloud data solutions. Establishes reusable ingestion, transformation, quality, governance, compliance, and orchestration processes. Partners with business and BI stakeholders to define data requirements, support financial and operational reporting, and enable AI use cases. Requires expertise in cloud and big data technologies, DevOps, CI/CD, data management, and governance, with preferred experience in Databricks, Snowflake, data warehousing, and finance-domain data.