Top Senior Data Engineer Jobs in Minneapolis, MN

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Leads the design, development, and operation of scalable data platforms and ETL/ELT pipelines using Python, PySpark, Databricks, Spark, Delta Lake, and cloud services. Responsibilities include data modeling, performance optimization, governance, lineage, quality validation, security, compliance, CI/CD, monitoring, AI/ML enablement, and data product delivery. Partners with stakeholders and mentors engineers while supporting reliable enterprise healthcare data solutions.

Own and evolve the Azure-based data platform for a large-scale telematics SaaS platform. Build high-throughput IoT ingestion, transformation, enrichment, storage, and analytics pipelines using Cosmos DB, Azure Data Explorer, Azure SQL, Fabric, Event Hubs, and Functions. Design scalable telemetry and multi-tenant data models, ensure pipeline reliability and observability, optimize costs and performance, implement governance and CI/CD, and collaborate with engineering, architecture, product, and leadership teams.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Build trusted, secure, and repeatable data pipelines, transformations, data products, retrieval layers, and semantic data assets. Assess data quality, lineage, governance, privacy, and access constraints; support AI retrieval, embeddings, vector stores, and grounding strategies. Create test and de-identified datasets, implement monitoring and quality checks, document data assets, and ensure reliable data for AI products, control-group testing, and production handoff.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Develops and maintains enterprise data warehouse solutions, relational database schemas, ETL pipelines, and automated data processes. Optimizes complex SQL queries, indexing, and execution plans for large datasets while supporting scalable data systems. Collaborates with analysts, product managers, QA, developers, and business stakeholders throughout solution design and delivery. Requires advanced SQL, data modeling, governance, SSIS, and data warehouse experience, with Azure Data Factory, ActiveBatch, and Snowflake preferred.

Designs and optimizes Power BI dashboards, semantic models, datasets, DAX measures, and reporting solutions for identity governance and administration analytics. Partners with ETL engineers and stakeholders to validate Parquet data from Azure Synapse and SailPoint sources, establish data-quality checks, monitor refreshes, define KPIs, and document data lineage. Supports compliance reporting, access governance, audit readiness, certification metrics, and Zero Trust analytics.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Designs, builds, and optimizes scalable data pipelines, ETL workflows, integrations, and reporting solutions. Develops SQL, Python, and Spark data-processing solutions using Azure Data Factory and Microsoft Fabric, while monitoring production reliability and performance. Collaborates with stakeholders, participates in code reviews, documents technical solutions, mentors junior engineers, and improves engineering practices. Requires strong data modeling, cloud data platform, Git, communication, and problem-solving skills.

Designs, develops, deploys, and supports scalable cloud data engineering solutions for HEDIS processing and healthcare quality reporting. Builds ETL pipelines, data integrations, SQL transformations, and AI-enabled workflow automation. Collaborates with stakeholders, auditors, and product teams to ensure data quality, regulatory compliance, and operational reliability. Troubleshoots production issues, supports cloud modernization, applies CI/CD and testing practices, and mentors engineers as a senior individual contributor.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Build and deliver Azure and Microsoft Fabric data solutions for clients. Responsibilities include developing data pipelines with Azure Databricks, Data Factory, Synapse Analytics, and Fabric; writing SQL, Python, and PySpark; supporting data integration, testing, deployments, troubleshooting, CI/CD, and infrastructure management. The role involves consulting with client stakeholders, collaborating with architects and developers, and delivering modern data platforms and reporting solutions.

Design and maintain Snowflake-based data platforms, cloud-native data lakes, and scalable ETL/ELT pipelines for asset-backed finance analytics. Orchestrate workflows with Dagster and AWS services, ensure data reconciliation and integrity, and support migration from legacy systems. Contribute to project planning, Jira backlog management, testing, cutover planning, and cross-functional go-live readiness.

Design and maintain Snowflake data pipelines and data lake architectures for asset-based finance analytics. Develop ETL/ELT workflows, validate and reconcile financial data, migrate legacy systems, and translate quantitative finance logic into scalable architectures. Support project execution through requirements breakdown, planning, testing, cutover, and go-live readiness. Collaborate across engineering, quantitative analytics, and stakeholder teams.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.