Top Data Engineer Jobs in Minneapolis, MN

Design, build, and maintain end-to-end data pipelines and ETL for enterprise data warehousing using Azure Data Factory, DBT, and Snowflake. Optimize SQL, support integrations and batch ingestion, mentor team members, document data interfaces, improve data quality and monitoring, and collaborate with stakeholders to translate business needs into scalable data solutions.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

Design, build, and maintain scalable batch and real-time data pipelines on an Azure data platform using Azure Data Factory, Databricks, and PySpark. Implement Medallion architecture (Bronze/Silver/Gold), enforce data quality and governance, optimize Spark jobs and cluster usage, monitor pipeline health, and collaborate with data science and reporting teams. Document lineage, handle CDC/incremental loads, and integrate streaming ingestion and validation checks to ensure reliable analytics and ML-ready datasets.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead and manage delivery of modern data ecosystems and managed data analytics programs. Oversee project planning, budgeting, quality, and client accounts. Mentor teams, drive data strategy, convert insights to business opportunities, and ensure standards and risk management across large transformations.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, build BI reports and dashboards (Oracle BI, QlikView, Spotfire), analyze data to produce strategic insights, support client engagements in regulated environments, and apply continuous process improvements to ensure data quality and integrity.

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.

Design, build, and operate scalable data platforms and ETL/ELT pipelines using Python, SQL, Airflow, and AWS. Optimize data warehouses, ensure data quality and governance, implement CI/CD and infrastructure-as-code, and collaborate with cross-functional teams while mentoring engineers.

Design, build, and maintain scalable ETL/ELT data pipelines and architectures across warehouses, lakes, and streaming environments. Ensure data governance, quality, and security. Lead delivery of analytics data products, optimize production systems, collaborate with stakeholders, and mentor junior data engineers.

Senior ETL Data Developer to design, build, test, and support Epic Clarity and enterprise ETL solutions using SSIS (and DataStage). Develop and optimize ETL workflows, implement standards, troubleshoot complex integration issues, document designs, and collaborate with reporting, analytics, and business teams to deliver data warehouse solutions and integrations.

Design, build, test, and operationalize complex data pipelines and multi-layer data architectures using Snowflake and dbt. Analyze source systems, perform data mapping and transformations, ensure data governance, quality, and security, resolve defects, optimize query and pipeline performance, and collaborate with engineering and business teams to support BI and AI/ML use cases.

Design, build, and support data architectures, ETL processes, and analytics/reporting (SQL, Alteryx, Tableau/Power BI). Perform data mining, modeling, cloud/platform work, vendor management, and lead/mentor team members while delivering complex reports and analytics to internal and external customers.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

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.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Designs, builds, and maintains scalable data infrastructure, streaming and batch pipelines, and data products. Works with stakeholders to model, transform, and deploy data solutions using cloud, streaming, and ETL frameworks while ensuring governance, observability, and automated CI/CD deployments.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, and maintain Microsoft Fabric-based enterprise data warehouse, ETL/ELT pipelines, and integrations. Ensure data quality, governance, and performant BI datasets while partnering with stakeholders and BI/Power BI teams. Provide technical leadership, troubleshoot pipelines, and recommend platform improvements.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead technical design and delivery of enterprise data warehouse and BI solutions. Own data architecture, complex ETL/ELT development, testing, performance tuning, and prototyping. Mentor teams, coordinate cross-functional resources and vendors, enforce best practices, and ensure data readiness and quality to enable analytics and business value.