Top Data Engineer Jobs in Milwaukee, WI

The Senior Data Engineer will design and optimize data infrastructures and pipelines, lead projects, mentor juniors, and enforce best practices.

Lead data platform architecture and delivery for healthcare-focused Lakehouse/Warehouse solutions. Drive Databricks/AWS design, governance, security (PHI/PII), ETL/Delta Live Tables, APIs, modeling (FHIR/OMOP), IaC (Terraform), pipeline performance, and cross-functional collaboration to deliver scalable, production-grade data products.

Design and implement enterprise Databricks/AWS data platform and lakehouse solutions for healthcare data. Build and optimize ETL pipelines (Delta Live Tables), medallion architectures, streaming and batch processing, data models (FHIR/OMOP), governance (Unity Catalog), security for PHI/PII, IaC with Terraform, and collaborate with cross-functional teams to deliver reliable, cost-effective external and internal data products.

Design and build Spark ETL pipelines on an AWS data platform, collaborate with stakeholders to deliver reliable large-scale data products, optimize and maintain data infrastructure, ensure data quality/governance, and manage platform operations and incident response.

Lead and scale the data analytics function, manage a distributed analytics team, deliver actionable insights, and oversee analytics projects.

The Senior Data Engineer will design and implement complex data models, collaborate on data governance, lead data initiatives, and mentor team members.

Lead design, build, and optimization of Sezzle's large-scale data warehousing and pipelines. Own data infrastructure KPIs/SLAs, improve Redshift performance, evolve the data stack, implement ETL/ELT workflows, and partner with cross-functional teams to deliver reliable, scalable datasets.

The Senior Data Engineer will develop data engineering solutions, manage ETL workflows, and establish a new data practice while collaborating with stakeholders.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

As a Senior Data Engineer, you will design and maintain scalable data pipelines, focusing on architecture, ingestion, storage, and analytics on GCP. You'll collaborate with stakeholders and optimize data structures for performance while ensuring adherence to data governance and compliance standards.

The Senior Data Engineer will focus on Data Modeling, utilizing SQL, Snowflake, and Apache Airflow to develop efficient data models and deliver data products.

Design, build, and maintain production data pipelines and tooling (CI/CD, testing, validation) using Snowflake, dbt, and orchestration tools. Model, transform, document, and optimize data to produce reliable curated datasets for analytics and solutions, enforce engineering best practices, and mentor teammates while ensuring scalability, observability, and data integrity.

Design and optimize BI semantic models and analytics workflows using PowerBI and Tabular Editor; build and transform large-scale datasets in BigQuery with dbt; implement CI/CD and deployment via GitHub and Azure DevOps; collaborate with stakeholders to deliver actionable visualizations and integrate Agentic AI into analytics pipelines.

Lead design, build, and support of cloud-based ETL, data warehousing, and reporting solutions. Develop/optimize ETL pipelines, data models, and queries; deploy containerized services on Kubernetes; automate workflows; mentor teammates; collaborate with architects, data scientists, and stakeholders to deliver scalable analytics and reporting products.

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.

Design, build, and maintain production data pipelines and the unified data model using Snowflake, dbt, and orchestration tools. Optimize, test, document, and monitor ELT workflows, enforce engineering best practices, mentor teammates, and deliver trustworthy data products for analytics and business decisions.

As a Senior Data Engineer at EverHealth, you will design and maintain data pipelines, enhance warehouse infrastructure, and ensure data quality to support reporting and analytics for healthcare products.

The Senior Data Engineer will design and build data pipelines on Azure Databricks, ensuring data security and governance while collaborating with clients and vendors on projects.

Design, build, and scale data pipelines and integrations for the TDX platform. Implement REST API integrations, ingest and unify customer data, optimize pipelines for performance and quality, ensure data security and governance, and collaborate with Product, Data Science, and customer teams to deliver reliable analytics-ready data.

Design and maintain ELT pipelines, build dimensional (star/snowflake) analytics models, implement data transformations and quality checks, document data lineage and governance, partner with BI users, and mentor junior engineers to deliver performant analytics for business reporting and KPIs.

Develop and maintain data pipelines and reporting tools, support real-time processing workflows, and collaborate with teams to enhance platform capabilities, utilizing Java, Python, and Groovy scripts.

The Data Migration Engineer will design and execute SQL-based ETL processes for migrating public safety data, ensuring high-quality data integration and collaboration with internal and external teams.

Implement client revenue cycle data into company systems by creating and configuring ETL processes, building data transformations and logical/physical schemas, documenting solutions in Jira, attending client meetings, and mentoring team members to ensure post-implementation data integrity.

The role involves building and optimizing data pipelines, modeling datasets, applying machine learning techniques, and supporting data strategies. Candidates should have experience with Python, data lakes, warehousing, and related technologies.

Design and deploy scalable data infrastructure and models, maintain CDC pipelines, build reporting tools and dashboards, write high-performance SQL, partner with product and engineering to enable enterprise-wide analytics and data-driven decision making.