Top Data Engineer Jobs in Minneapolis, MN

The Data Engineer is responsible for developing data infrastructure, building data pipelines, managing data quality, and integrating analytics tools to support design and project operations.

The Data Engineer will manage data extraction pipelines, modernize data systems, implement monitoring tools, optimize workflows, and support compliance requirements using various tools and platforms.

The Data Engineer I at Storable supports the design and maintenance of data pipelines, works with ETL processes, optimizes workflows, and collaborates with various teams to enhance data accessibility and reliability.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Support and administer Oracle databases, optimize data warehouse performance, and collaborate on data technology solutions within payment systems infrastructure.

As a Data Engineer, you will build and optimize data pipelines, manage ETL workflows, and ensure efficient data access and quality within the infrastructure.

The Junior Data Engineer will analyze data, build and maintain data pipelines, support CI/CD processes, and assist in IT modernization efforts for a Federal client.

As an Analytics Product Engineer, you will lead product strategy for data products, design scalable data models, and develop transformation pipelines while collaborating with stakeholders and improving analytics capabilities.

Design and manage data processing systems, develop data models, ensure data integrity, collaborate on data sources, and implement data governance practices.

Lead design and delivery of enterprise data centralization and automation for reporting and BI. Build and maintain ETL/SSIS pipelines, data orchestration, file delivery and quality controls, follow SDLC/agile, mentor junior engineers, and partner with business stakeholders to capture requirements and ensure timely data delivery.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

Design, implement, optimize, and maintain scalable, high-availability database solutions for a SaaS healthcare product. Integrate client data, resolve complex data issues, refactor systems for Azure, optimize MSSQL performance, participate in code reviews, and deliver features within Agile/Scrum to ensure reliable, high-performance data services.

As a Data Engineer at LiveKit, you will manage the analytics infrastructure, develop scalable GCP-based data pipelines, and ensure effective data movement and transformation processes, while collaborating with the Analytics and engineering teams.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Build and maintain cloud data pipelines and ML feature/model workflows in Snowflake and AWS. Implement data ingestion, transformations, quality checks, monitoring, and documentation. Collaborate with data scientists, analysts, and product teams to support analytics, audience insights, rapid prototyping, and reliable production deployments.

Design, build, and maintain ELT pipelines and analytics-ready data models to support reporting, KPIs, and downstream ML use cases; implement data quality, optimize pipeline performance, document lineage, and ensure governance and privacy.

The Data Engineer at Netflix builds systems for efficient data processing and modeling, collaborating with various teams to empower analytical insights and product features.

Design, build, and maintain scalable, reliable data pipelines and services. Write well-tested Python and SQL code, work with orchestration (Airflow), containers, and cloud infrastructure. Collaborate cross-functionally to deliver data products and improve development processes.

Build and maintain Databricks-based ETL/ELT pipelines, transform and model data, optimize workflows, monitor and troubleshoot pipelines, and collaborate with cross-functional teams.

The Principal Data Engineer will design and implement enterprise data frameworks, mentor teams, and ensure data quality while collaborating with stakeholders.

The Data Engineer I will manage data warehouses, extend data pipelines, write SQL and Python scripts, and support data operations.

The Data Engineer will build scalable privacy-centric data frameworks, engineer data pipelines, and ensure compliance with privacy regulations at Netflix.

The Data Engineer III will design and maintain data infrastructure, ensuring efficient data loading and compliance while mentoring junior engineers and collaborating with Analytics teams.

The Lead Data Engineer will design and implement data solutions for imagery models, lead engineering efforts, collaborate with stakeholders, and ensure data quality and governance while optimizing data pipelines.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.