Top Remote Data Engineer Jobs in Minneapolis, MN

The Data Ingestion Specialist will manage the ingestion of healthcare data, improve data models, debug issues, and collaborate with team members to maintain pipeline reliability and efficiency.

Design, build, and maintain scalable ETL/ELT pipelines and product-level data architectures. Improve pipeline performance and data freshness, lead code/design reviews, mentor engineers, and collaborate with Data Science, Product, and Engineering to make data reliable and accessible.

Design, build, and maintain scalable, secure data architectures, models, and ETL pipelines. Integrate diverse data sources, optimize performance and data quality, implement governance, monitor production SLAs, and collaborate with data scientists, analysts, and product teams. Provide technical leadership, documentation, and training to improve engineering practices across the organization.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

Design, develop, and support enterprise analytical and operational data solutions (ETL/ELT, data models, integrations, reports). Lead metric/technical design, manage medium-to-large projects, provide production support, documentation, training, and mentor junior team members. Maintain certifications and stay current with EHR/ERP, cloud, SQL, and BI technologies.

Lead a team of 5-6 data engineers to design and implement scalable data pipelines (Airflow), manage Snowflake data architecture, build real-time streaming solutions with Kafka/Spark Streaming, develop and integrate APIs, enforce code quality, and drive technical decisions and best practices.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead scaling of Arbital's AWS/Databricks lakehouse and Spark pipelines, implement actuarial healthcare calculations, own Airflow orchestration and CI/CD, ensure data quality and HIPAA-compliant PHI handling, partner with data scientists to deploy ML models, and set platform architecture and engineering standards.

Design, build, and maintain scalable backend systems and data pipelines to generate point-of-care insights; collaborate with product and engineering stakeholders; mentor junior engineers; ensure solutions are secure, performant, maintainable, and support analytics and unified metrics collection.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Build, maintain, and optimize cloud data pipelines and dbt models; design Airflow DAGs; implement dimensional models and BI dashboards; integrate healthcare data with HIPAA-compliant practices; contribute to RAG pipelines and MLOps workflows; review contractor code, monitor data quality, and document designs following governance standards.

Remote entry-level Big Data Engineer role building data acquisition and munging pipelines. Requires strong Python3 (NumPy, Pandas), advanced SQL, scalable algorithm and data-structure implementation on distributed clusters, converting code to functional paradigms, and the ability to learn PySpark or Kafka quickly. Product-development, startup experience and real-world pipeline projects preferred.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

Lead data engineering for a healthcare scheduling platform rebuild: map legacy multi-TB schema, design target schema, build translation layer (views/materialized views and write mappers), ensure data consistency during parallel systems, plan per-tenant cutover with verification and rollback, tune DB performance, instrument data quality/lineage/audit, and mentor engineers.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

As a Senior Data Engineer, you will manage data pipelines, ensure data quality, partner with teams for data-driven decisions, and improve the data platform while maintaining data integrity.

The Senior Data Engineer will create and manage data pipelines, ensuring data quality, collaborating with teams for data-driven decisions, and evolving the data platform.

Lead design and evolution of Foodsmart's data platform and pipelines, set architecture and standards for data quality, governance, and observability, enable analytics and ML at scale, mentor engineers, and drive platform-wide performance and reliability improvements.

Lead delivery of enterprise data governance, MDM, and modern data-platform components including Snowflake, CDC pipelines, schema-first contracts (JSON Schema/Avro/Protobuf), data catalog and PII classification, CI/CD schema management, AWS data services, and day-to-day leadership of an offshore delivery team.

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.

Lead and define architectural solutions for data pipelines, mentor teams, optimize ETL/ELT processes, and ensure data governance in a client-facing environment.

Design, build, and maintain large-scale data pipelines and ETL/ELT processes using Redshift, Azure Data Factory, Hadoop/Hive, Spark/Databricks, and Snowflake on cloud platforms (GCP). Implement Scala-based data processing, optimize performance, and support analytics and data warehousing needs.

Build and own data platform infrastructure and scalable pipelines (ingestion, orchestration, transformation, delivery). Define governance, observability, and AI-first architecture. Optimize performance, ensure data quality, mentor engineers, and integrate AI-assisted tooling.