Top Data Engineer Jobs in Minneapolis, MN

Designs, develops, validates, and documents analytics data solutions for diverse client environments. Builds Snowflake, SQL, dbt, and AWS ingestion and transformation pipelines; monitors data quality and performance; troubleshoots complex technology issues; and supports reporting and business insights. Manages project scope, budgets, documentation, governance, client communication, implementation planning, and quality assurance while collaborating across teams and identifying opportunities for additional solutions.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Designs, builds, deploys, and maintains enterprise-scale Azure cloud data solutions and healthcare analytics platforms. Responsibilities include developing data pipelines and warehouses with Databricks, Snowflake, Kafka, and Azure Data Factory; applying AI productivity tools; evaluating emerging technologies; enforcing governance, testing, security, and quality standards; and optimizing performance. The role collaborates with product and engineering teams to deliver commercially available healthcare software and supports scalable data architecture.

Own the architecture, development, and operations of Cargill’s enterprise data platform. Build and optimize Azure and on-premises ETL/ELT pipelines, manage databases and migrations, implement CI/CD, improve performance and reliability, and create embedded Superset dashboards. Monitor platform health, data quality, security, and costs while integrating emerging AI capabilities. Mentor data professionals and make architectural decisions across global teams.

Leads end-to-end clinical data exchange implementations with healthcare providers, vendors, laboratories, and HIEs. Responsibilities include requirements gathering, data integration development, testing, deployment, production support, compliance with healthcare data standards, dashboard development, operational metrics, automation, incident management, and mentoring. The role requires coordinating multiple concurrent technical implementations and translating business needs into scalable clinical data solutions.

Designs, develops, and maintains enterprise data integrations, ETL/ELT pipelines, and analytics infrastructure using Informatica and Snowflake. Partners with data scientists, analysts, IT, and business stakeholders to deliver trusted data for reporting, forecasting, business intelligence, and data science. Responsibilities include platform administration, security and access management, troubleshooting, data governance, quality initiatives, architecture reviews, and continuous improvement.

Designs and maintains scalable SQL and NoSQL database systems and cloud-native data platforms. Builds real-time and batch processing pipelines, data lakes, data meshes, dashboards, migration tools, and streaming architectures. Optimizes partitioning, sharding, replication, queries, indexes, capacity, security, and data models for high-volume workloads. Collaborates with product and engineering teams, supports data governance and quality, troubleshoots distributed systems, and implements CI/CD for database and pipeline deployments.

Designs, develops, maintains, and optimizes the enterprise data platform, Microsoft Fabric data warehouse, ETL/ELT pipelines, integrations, data models, and reporting datasets. Ensures data quality, governance, security, compliance, and platform performance while collaborating with business intelligence and application teams. Provides technical leadership, mentoring, troubleshooting, documentation, and recommendations for improving data architecture and engineering processes. This is an on-premise role based in Bloomington.

Build and optimize scalable data pipelines and models for pharmacy, claims, pricing, reimbursement, MTM, and rebate data. Responsibilities include data ingestion, cleansing, transformation, validation, governance, quality monitoring, root-cause analysis, testing, code reviews, performance tuning, and cloud data architecture. The role collaborates with analysts, data scientists, engineers, and healthcare stakeholders to deliver reliable data assets, support analytics and machine learning, and improve DataOps practices.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Own and evolve the Azure-based data platform for a large-scale telematics SaaS platform. Build high-throughput IoT ingestion, transformation, enrichment, storage, and analytics pipelines using Cosmos DB, Azure Data Explorer, Azure SQL, Fabric, Event Hubs, and Functions. Design scalable telemetry and multi-tenant data models, ensure pipeline reliability and observability, optimize costs and performance, implement governance and CI/CD, and collaborate with engineering, architecture, product, and leadership teams.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Designs, builds, operates, and modernizes secure ETL/ELT data pipelines for public health interoperability. Responsibilities include using NiFi, Kafka, Python, and SQL; deploying pipelines on Azure or AWS; implementing CI/CD with GitHub Actions; monitoring with Grafana; troubleshooting production systems; supporting legacy migrations; and ensuring data security, compliance, scalability, and reliability. The role collaborates with architects, integration engineers, data scientists, DevOps/SRE, and product teams.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Build and maintain production ETL pipelines for structured and unstructured data, integrate machine learning models for enrichment, develop data transformations and quality monitoring, design warehouses and dimensional models, establish governance frameworks, and contribute to data architecture and platform improvements. Collaborate with data scientists, analysts, and engineering teams to deliver reliable, actionable intelligence.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Build trusted, secure, and repeatable data pipelines, transformations, data products, retrieval layers, and semantic data assets. Assess data quality, lineage, governance, privacy, and access constraints; support AI retrieval, embeddings, vector stores, and grounding strategies. Create test and de-identified datasets, implement monitoring and quality checks, document data assets, and ensure reliable data for AI products, control-group testing, and production handoff.

Designs and optimizes Power BI dashboards, semantic models, datasets, DAX measures, and reporting solutions for identity governance and administration analytics. Partners with ETL engineers and stakeholders to validate Parquet data from Azure Synapse and SailPoint sources, establish data-quality checks, monitor refreshes, define KPIs, and document data lineage. Supports compliance reporting, access governance, audit readiness, certification metrics, and Zero Trust analytics.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop warehouse models, transformations, automation, data quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Designs and maintains a scalable data platform supporting analytics, machine learning, and AI workloads. Builds tooling for reliable data pipelines, semantic layers, data agents, LLM applications, and evaluation systems. Ensures data quality, availability, observability, and reliability while establishing modeling, testing, and platform best practices. Collaborates with Data Science and Product Engineering, leads solutions from design through implementation, and contributes individually in a fast-paced startup environment.

Build, test, and maintain batch and event-driven data pipelines and ETL/ELT workflows. Transform and validate large datasets, troubleshoot data-quality issues, improve workflow reliability, and support platform modernization. Collaborate with engineering, product, and data stakeholders while following strong practices for version control, testing, monitoring, documentation, and code review. The role may occasionally require work outside standard business hours.