Top Senior Data Engineer Jobs in Minneapolis, MN

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 end-to-end data pipelines and integrations using Azure Data Factory, DBT, and Snowflake. Optimize SQL and Snowflake performance, support batch ingestion and API-based integrations, mentor engineers, produce technical documentation and runbooks, and ensure data quality, monitoring, and secure compliant data transfers.

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.

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, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.

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.

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 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 data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

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, build, and maintain enterprise ETL and data transformation pipelines to support Medicaid analytics and federal reporting. Optimize data processing with Python, Spark/Databricks, and relational platforms; ensure data validation, reconciliation, auditability, and production support. Collaborate across architects, analysts, QA, and BI teams during cloud migration and modernization efforts.

Design, build, and maintain scalable data pipelines and data warehouse solutions on Azure. Perform source data analysis, profiling, ETL development with PySpark, support migrations from on-prem to cloud, troubleshoot production issues, collaborate in Agile teams, and contribute to ML/Generative AI initiatives and Power Apps development.

The Principal Data Engineer at HealthPartners leads data pipeline development, collaborates with teams to enhance data solutions, and implements best practices for data management and integration in healthcare services.

Design, build, test, and operationalize complex data pipelines and ETL solutions using Databricks and AWS. Analyze source data, enforce data governance, security and quality, resolve defects, collaborate with stakeholders, and support AI/ML and BI use cases.

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.

As a Data Engineer II, you will build and operationalize complex data solutions, ensuring data quality and governance while supporting AI and business intelligence applications.

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 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.

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.

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.

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.