Top Data Engineer Jobs in Minneapolis, MN

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

Design, build, and maintain scalable ETL/ELT data pipelines and analytical platforms on AWS and Databricks. Migrate legacy workflows to cloud-native services, implement data lake and catalog solutions, enable AI/RAG/vector search, ensure FedRAMP/NIST compliance, optimize performance, and mentor junior engineers while collaborating in Agile teams.

Design, build, and operate Alpaca's scalable data platform and lakehouse infrastructure. Build low-latency streaming and CDC ingestion, batch pipelines landing in Iceberg, and tooling for BI and AI consumers. Manage IaC, Kubernetes deployments, monitoring/incident response, and partner with DevOps and analytics teams to meet growing data requirements.

Lead Data Engineer responsible for defining architecture, engineering standards, and best practices for the enterprise data platform, collaborating with distributed teams for modernization and scalability.

Design, build, and maintain scalable, reliable data systems and pipelines. Partner with Product, Sales Ops, and Finance to deliver analytics and self-service tooling. Maintain SLOs, improve pipeline reliability, implement testing and documentation, and contribute to data platform strategy and operations.

Design, build, and own end-to-end data systems and pipelines powering payments, fraud response, and reporting. Implement streaming and batch integrations, APIs, and workflow orchestration. Define data correctness with monitoring, alerting, and tests. Create reusable patterns and collaborate with stakeholders to clarify requirements and tradeoffs.

Design, build, and maintain Snowflake data models, warehouses, and ELT/ETL pipelines; develop backend services in Python and C#; manage CI/CD and deployments; troubleshoot production issues; collaborate with clients and mentor junior engineers.

Lead design and implementation of CDC-based real-time and batch data pipelines, own Snowflake warehouse strategy, implement IaC for the data platform, build reporting layer, define data quality/monitoring, collaborate with product and engineering, and mentor engineers while evolving platform architecture and introducing new technologies.

Design and own Ceresti’s end-to-end data architecture, including secure raw-data storage, validated file and API ingestion, Postgres pipelines, and curated analytics layers. Establish governance for HIPAA-protected data, data quality, lineage, observability, and access controls. Build ML feature tables and embedding capabilities, collaborate on data contracts, support Agile delivery, and mentor engineers on SQL and data-system design.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Designs and maintains Snowflake-based cloud data pipelines and lake architectures for asset-based finance and mortgage servicing analytics. Leads legacy-system migrations, ETL/ELT development, data validation, reconciliation, and financial integrity controls. Translates business requirements into Jira epics and user stories, supports project planning, testing, cutover, and go-live readiness, and coordinates with business, finance, operations, and technical stakeholders.

Design and scale Zip’s enterprise data and AI platform across batch, streaming, structured, and unstructured workloads. Build reliable pipelines, AI-ready data products, semantic models, vector and hybrid search, and RAG capabilities. Establish standards for modeling, testing, governance, security, observability, and automation. Partner with engineering, data science, product, and business teams while mentoring engineers and influencing platform architecture.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.

Build and lead complex data integration projects from client onboarding through ongoing platform usage. Analyze and resolve data discrepancies, troubleshoot SIS and SSO integrations, develop internal Retool applications, perform code reviews, prioritize backlogs, manage projects, and mentor teammates. The role requires advanced SQL, development expertise, technical leadership, and collaboration with internal stakeholders, with Ruby on Rails, AWS, Git, and K-12 education experience preferred or desired.

Leads enterprise data engineering initiatives by designing and optimizing data pipelines, data lakes, warehouses, and analytics infrastructure. Uses Snowflake, Informatica, SQL, and related technologies to integrate diverse data sources, improve data quality, secure PII, and support analytics and data science teams. Establishes governance, monitoring, scalability, performance, and cost-efficiency practices while mentoring teammates and partnering with stakeholders on complex technical solutions.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Lead architecture and execution of Faire's data platform and analytics capabilities. Build and operate scalable, secure ETL/ELT, streaming, storage, compute, orchestration, and data warehousing at petabyte scale. Drive performance, cost, monitoring, governance, and self-serve analytics; influence cross-functional teams and mentor engineers.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.

Technical lead for Finance Technology data engineering: design and build scalable batch and real-time data pipelines (Hadoop/Spark/PySpark/Scala/Java), optimize data models and APIs, drive cloud-based data architecture (AWS/GCP/Azure), enforce governance/security, and mentor engineers to deliver enterprise financial data services.

Design and maintain healthcare data integration pipelines, models, and validation processes for claims, billing, clinical, pharmacy, EMR, FHIR, and value-based care data. Collaborate with clients and internal analytics, product, and customer success teams to interpret data, document business rules, support implementations, and build reusable Ursa data models. Apply healthcare domain expertise, data security practices, HIPAA compliance, and appropriate judgment when using AI-assisted tools.