Top Data Engineer Jobs in Minneapolis, MN

Lead migration of on-premises databases and ETL workloads to Azure and Snowflake. Design scalable data platforms using Azure Data Factory, Databricks, ADLS, Logic Apps, Blob Storage, and Python. Modernize SSIS, T-SQL, and legacy ETL solutions while developing AI-powered capabilities using LLMs, semantic search, vector databases, document intelligence, and RAG architectures. Work independently, collaborate effectively, adopt new technologies, and travel up to 10%.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Build and maintain data integration workflows and ETL/ELT pipelines using Google Cloud Data Fusion. Transform usage data, write SQL and data-processing scripts, monitor workflows, troubleshoot failures, profile data, and document architectures. The role also supports AI-powered data solutions, responsible AI practices, pipeline optimization, and continuous process improvement while collaborating with technical teams in a remote environment.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Architects, develops, deploys, and optimizes enterprise-scale data platforms, schemas, pipelines, data models, and integrations. Leads SQL engineering, ETL/ELT frameworks, API ingestion, cloud-native solutions, database performance tuning, analytics, business intelligence, and data governance. Supports Power BI and other BI tools while providing technical leadership across data engineering initiatives. Knowledge of disputes, chargebacks, fraud operations, and case-management systems is preferred.

Design and scale batch and streaming data pipelines, ETL/ELT workflows, data models, warehouses, quality frameworks, dashboards, and AI/ML feature pipelines. Automate reporting and analytics while collaborating with product, engineering, and operations teams. The role also includes internal consulting practice participation, thought leadership, client case studies, and contribution to digital transformation initiatives.

Build and maintain Databricks data pipelines across bronze, silver, and gold layers. Ingest APIs, logs, billing exports, and reference data; implement attribution logic, governance, data quality monitoring, and cost optimization. Manage Unity Catalog permissions, lineage, refresh schedules, incremental processing, and CI/CD workflows while supporting multi-cloud storage and high-volume caller-identity data.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Designs, builds, and maintains scalable data pipelines, relational databases, data integrations, and data warehousing solutions. The role develops ELT processes, ensures data quality and governance, troubleshoots pipeline and database issues, and documents workflows. It collaborates with Data Operations, Quality Engineering, Business Intelligence, architects, and subject matter experts to deliver reliable data assets aligned with business needs.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Build, deploy, and operationalize data pipelines and solutions supporting AI, machine learning, and business intelligence. Responsibilities include data transformation, cleansing, governance, security, quality management, source analysis, testing, incident resolution, and defect remediation. The role collaborates across teams, supports data products, educates users, and applies software engineering and DevOps practices to deliver reliable analytical data solutions.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Designs, develops, maintains, and optimizes complex data systems, SQL/ETL processes, reporting solutions, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, data warehousing, troubleshooting pharmacy claims, technical specification development, software testing, cloud integration, stakeholder collaboration, Agile participation, and mentoring. The role requires strong SQL Server, dimensional modeling, cloud database, Python, PowerShell, and healthcare data standards expertise.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Build and operate ETL and real-time pipelines connecting veterinary practice systems, billing platforms, and third-party APIs. Design data models and a warehouse supporting hospital analytics, finance, and reporting. Establish standards for testing, documentation, monitoring, and data quality, while troubleshooting production data issues. Partner with Finance to improve Sigma self-service reporting and define scalable integration patterns as the company’s first data engineer.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.