Top Data Engineer Jobs in Minneapolis, MN

Designs, builds, and maintains scalable data systems, streaming and batch pipelines, data infrastructure, data models, and deployment pipelines. Develops data products using cloud-based technologies, optimizes existing architectures, improves data formats and accessibility, and collaborates with analytics teams and stakeholders to deliver reliable solutions for reporting and decision-making.

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 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.

Build and operate production-grade ELT pipelines from product, Salesforce, and third-party systems into Snowflake using dbt. Ensure testing, monitoring, schema versioning, governance, privacy, access controls, data quality, cost, and performance. Support AI initiatives with trusted data, contribute to warehouse and semantic-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

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.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

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.

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 scalable batch and streaming data pipelines using Python, PySpark, SQL, Databricks, Kafka, and Azure Data Factory. Provide production support, incident resolution, vulnerability remediation, platform upgrades, and reliability improvements. Optimize data models and processing workloads, implement automation and AI-enabled solutions, support CI/CD and cloud modernization, and ensure healthcare data security, governance, and privacy compliance.

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.

Design, develop, and maintain scalable batch and streaming data pipelines using Python, PySpark, SQL, Databricks, Kafka, and Azure services. Provide production support, incident resolution, vulnerability remediation, platform upgrades, and reliability improvements. Optimize data models and workloads, implement automation and AI-enabled solutions, support CI/CD and monitoring, and ensure security, governance, and healthcare privacy compliance. Collaborate with business and technology partners on enterprise data solutions.

Build and maintain scalable batch and real-time data pipelines and services for finance systems. Develop ETL workflows, data models, APIs, and distributed processing solutions using Spark, cloud platforms, and SQL. Ensure data governance, security, and quality while collaborating with engineers, product teams, and business stakeholders to troubleshoot, optimize, and improve system reliability.

Designs, builds, and operates scalable data systems and products on Cargill’s AWS-native Minerva platform. Develops batch and streaming pipelines, data models, lakehouse architectures, automated deployment pipelines, and governed analytics solutions. Migrates workloads from legacy CDP and Impala platforms, validating schemas, row counts, aggregates, and historical data accuracy. Collaborates with data and analytics teams across supply chain, procurement, manufacturing, commercial, and LATAM domains.

Administer, optimize, and modernize enterprise SQL Server, Azure SQL, MongoDB, and MongoDB Atlas environments. Responsibilities include performance tuning, high availability, disaster recovery, security, monitoring, migrations, automation, architecture guidance, and on-call support. The role also leads database projects and adopts AI-assisted engineering solutions to improve reliability and operational efficiency.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Build and optimize Alteryx workflows and data ingestion processes; extract, transform, normalize, and integrate enterprise data from multiple sources. Automate repetitive processing, ensure data quality, troubleshoot integrations, document workflows, and deliver datasets and analytical insights for internal business stakeholders. The role may also develop Tableau visualizations and use Python, PowerShell, or Bash scripting.

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Build and operate cloud data capabilities, including ingestion pipelines, transformations, data models, curated data products, and data-quality controls. Use Snowflake, dbt, SQL, and Python to deliver governed data for analytics, applications, automation, and AI workflows. Implement testing, CI/CD, documentation, lineage, access controls, monitoring, incident resolution, and performance optimization. Collaborate with engineering, analytics, governance, security, and business teams in an Agile, product-oriented environment with active AI-assisted development.

Design and operate scalable, cloud-native data pipelines and orchestration systems for imaging, biomarker, and healthcare data. Build Databricks and distributed processing workflows, integrate DICOM data into data lakes, and implement validation, observability, monitoring, and fault tolerance. Collaborate with data science, AI, R&D, product, and data management teams to deliver secure, compliant, reliable data for analytics and machine learning.