Top Remote Data & Analytics Jobs in Minneapolis, MN

Architects and scales enterprise data models, dimensional models, dbt transformation pipelines, semantic layers, and BI datasets. The role focuses on data quality, testing, observability, governance, metric standardization, documentation, performance optimization, and secure access controls. It partners with executives and cross-functional teams, leads analytics engineering initiatives, troubleshoots complex data issues, owns domain models, and mentors engineers while advancing the company’s analytics platform strategy.

Serves as the technical and client-facing link between Inca Digital’s data engine and high-stakes financial and government clients. Conducts OSINT, web scraping, enhanced due diligence, blockchain and crypto investigations; analyzes large datasets; audits and synthesizes technical findings into intelligence reports; briefs executive stakeholders; and coordinates cross-functional teams to align products with client needs.

Designs, develops, maintains, and optimizes complex data systems, SQL/ETL processes, reporting solutions, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, data warehousing, troubleshooting pharmacy claims, technical specification development, software testing, cloud integration, stakeholder collaboration, Agile participation, and mentoring. The role requires strong SQL Server, dimensional modeling, cloud database, Python, PowerShell, and healthcare data standards expertise.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Leads the data organization across data engineering, analytics, reporting, and data science. Defines data strategy, architecture, governance, quality, and platform roadmaps; manages and develops technical teams; partners with business and engineering leaders; and guides scalable AWS-based data infrastructure, batch and real-time pipelines, and data products that support strategic growth.

Develops and maintains dashboards, reports, data tools, and data infrastructure for Consulting. Analyzes client, project, portfolio, capacity, compliance, and outcomes data to identify trends and support decisions. Maintains data integrity across Salesforce, Resource Hero, Power BI, and related systems; establishes governance standards; trains staff; and manages consulting projects, workflows, budgets, timelines, and deliverables.

Design and deliver modern data platforms, scalable and secure ingestion and transformation pipelines, durable data models, streaming systems, and reporting layers. Partner directly with clients and business stakeholders to translate requirements into architecture, explain tradeoffs, and own delivery outcomes. The role includes production implementation across cloud data platforms, orchestration, governance, real-time technologies, and analytics or AI data consumption.

Collects and validates patient-level clinical data from primary care clinics, maintains run and SPC charts, prepares recurring reports, and supports PDSA improvement cycles. Uses Excel, Google Sheets, and AI tools to clean data, synthesize feedback, identify trends, and improve workflows. Collaborates with clinic staff, project leads, and partners while ensuring data integrity, HIPAA compliance, and accurate reporting.

Build and operate ETL and real-time pipelines connecting veterinary practice systems, billing platforms, and third-party APIs. Design data models and a warehouse supporting hospital analytics, finance, and reporting. Establish standards for testing, documentation, monitoring, and data quality, while troubleshooting production data issues. Partner with Finance to improve Sigma self-service reporting and define scalable integration patterns as the company’s first data engineer.