Top Data Engineer Jobs in Minneapolis, MN

Design and execute data testing and validation for the National Data Warehouse. Create and run test scripts, document results, track defects, collaborate with business and technical teams, define acceptance tests, and recommend remediation. Build solutions using cloud data platforms and maintain strong IT-business relationships.

Design, build, and operate cloud-native ETL/ELT pipelines and data platforms (Databricks/Snowflake). Ensure data quality, governance, performance, and enable analytics/AI/ML through trusted, analytics-ready datasets. Collaborate with product and analytics teams, apply software engineering best practices, modernize legacy pipelines, and mentor junior engineers.

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.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

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.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Lead design, development, and implementation of data product features and reusable frameworks. Collaborate with architecture and stakeholders, drive technical decisions, deliver product implementations, and troubleshoot production issues. Emphasizes RESTful APIs and cloud/container technologies.

Lead Data Engineer builds and optimizes scalable data pipelines, implements robust data modeling and feature pipelines, enforces data quality/lineage, and provides tooling, runbooks, and enablement to drive predictive analytics and operational insights for technology leadership.

Design, build, optimize, and maintain scalable batch and streaming data pipelines using Databricks, Spark, and Python. Collaborate with product, architecture, and analytics teams to deliver reliable, observable datasets, improve data quality and performance, and support downstream reporting and analytics. Contribute to CI/CD, governance, code reviews, and production troubleshooting.

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.

Design and implement scalable ETL/ELT pipelines for structured and unstructured data, ensure data quality for bot training and real-time decisions, integrate data with conversational AI platforms, monitor and maintain voice bot systems, implement logging/monitoring, optimize data flows, and collaborate with AI/ML, DevOps, and compliance teams.

Design, build, and maintain scalable data pipelines and transformations using Snowflake, Airflow, and CI/CD. Ensure data quality, de-identification, monitoring, and optimization. Collaborate with architects, analysts, and stewards to meet business requirements and implement robust, production-ready data solutions.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

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.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.