Top Senior Data Engineer Jobs in Minneapolis, MN

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 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 build data infrastructure and pipelines using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, transformation, and analytics to deliver insights.

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.

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 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.

Design, build, and maintain data pipelines, transformations, and retrieval layers. Ensure data quality, lineage, metadata, security, and governance. Support AI initiatives (embeddings, vector DBs, RAG), create de-identified test datasets, and prepare production-ready data products for steady-state ownership.

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.

Design, build, and maintain scalable data pipelines and AI-powered solutions using Python, Spark, Scala, Kafka, Databricks, and Azure Data Factory. Focus on KTLO activities including vulnerability remediation, tool/version upgrades, data security, and platform stability. Apply data warehousing and dimensional modeling best practices, optimize query and pipeline performance, support real-time streaming applications, and automate workflows with Airflow while collaborating with cross-functional teams.

Design, build, and maintain scalable batch and streaming data pipelines and data models using Python/PySpark, Snowflake, and cloud platforms. Optimize performance, implement orchestration (Airflow), enforce data governance/metadata standards, create data quality frameworks, support CI/CD and DevOps, monitor production pipelines, and collaborate with stakeholders to evolve the enterprise data platform.

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.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

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.

Lead development and delivery of data engineering solutions for clients: design data architectures, build and maintain ETL pipelines, apply advanced analytics, implement BI visualizations, coach teams, manage managed-services SLAs, and engage stakeholders to validate and refine outcomes.

Lead data engineering engagements delivering ETL/ELT solutions, pipelines, and cloud analytics. Build and maintain architectures (DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate/CDC, S3/Glue), oversee BI and visualization, manage client accounts and teams, mentor junior staff, ensure project delivery and quality, and drive process and technology adoption.

Design, develop, and deploy AI-powered solutions and manage data infrastructure. Responsibilities include Snowflake administration, performance engineering, operations/on-call support, automation/DevOps, lifecycle management, security and compliance, monitoring, backup/recovery, incident/change management, SQL development, and hands-on Python coding.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Design, build, and operate cloud-native, domain-oriented data products and pipelines across Azure and AWS using Databricks and Snowflake. Lead migration from legacy platforms, implement batch and streaming processing with Spark, ensure security and governance compliance, promote best practices (CI/CD, testing, observability), and mentor junior engineers while collaborating with product, analytics, and security teams.

Designs, builds, and operationalizes complex Databricks/AWS data solutions and pipelines. Performs data analysis, cleansing, testing, and incident resolution while incorporating governance, security, and quality. Collaborates across teams, supports end users, and provides technical leadership to deliver production-ready data products for analytics, ML, and AI use cases.

Design, build, test, and operationalize complex data pipelines and transformations using ETL tools and cloud platforms. Ensure data quality, governance, and security while collaborating across teams, troubleshooting incidents, and mentoring engineers to deliver scalable data solutions for analytics and ML.

Design, build, test, and operationalize data pipelines and solutions using cloud technologies. Ensure data quality, governance, and security; analyze sources; resolve defects; collaborate across teams; support AI/ML and BI use cases; and document and educate users on data products.

Design, build, test, and operationalize data pipelines and solutions. Analyze data sources, apply transformations and quality controls, resolve incidents, and collaborate with teams to deliver data products supporting analytics, BI, and AI/ML use cases.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Build, test, and operationalize complex data pipelines and solutions on cloud platforms. Analyze and transform data, ensure data governance, security, and quality, troubleshoot defects, and collaborate across teams to support analytics, ML/AI, and business intelligence needs.