Top Data Engineer Jobs in St. Louis, MO

The Data Engineer will enhance the Enterprise Data Warehouse, modernizing ETL processes, optimizing data transformations, ensuring data quality, and providing production support while collaborating with cross-functional teams.

Design, build, and operate Spark-based ETL pipelines on AWS. Collaborate with stakeholders to deliver reliable data products, ensure data quality and governance, optimize performance, and maintain operational excellence of the data platform.

Design, build, and optimize distributed query solutions using Starburst/Trino. Enable secure, governed data access across cloud and on-premises sources, implement data federation and query optimization, support IAM and governance in a federal environment, develop backend services and CI/CD, and troubleshoot performance in cloud-native systems.

The Data Engineer will manage data extraction pipelines, modernize data systems, implement monitoring tools, optimize workflows, and support compliance requirements using various tools and platforms.

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.

As a Senior Data Engineer, you will build and maintain the data platform infrastructure, oversee data lakehouse architecture, and collaborate with product engineering to ensure reliable data delivery and adherence to data contracts.

The Staff Data Engineer will manage data governance, ETL/ELT operations, design security for reporting, and support ML workloads, while evolving a self-service reporting platform.

Design and maintain data pipelines for analytics and operational decision-making. Collaborate with teams to ensure reliable data systems and governance, utilizing Python and SQL.

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.

As a Data Engineer, you will build and optimize data pipelines, manage ETL workflows, and ensure efficient data access and quality within the infrastructure.

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.

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.

The Senior Data Engineer will design, implement, and optimize scalable data solutions, working with data pipelines and collaborating with cross-functional teams in healthcare.

The Senior Data Engineer will design and implement data architecture, create ETL pipelines, ensure data integrity, and support data democratization and operational tasks, collaborating with cross-functional teams to resolve data-related issues.

The Senior Data Engineer will lead BI development, enhance Power BI architecture, mentor developers, and ensure efficient delivery governance for BI projects.

The Senior Data Analytics Engineer will design and maintain data pipelines, optimize data models, and implement best practices for data governance and performance in a SaaS environment.