Top Remote Data Engineer Jobs in St. Louis, MO

Design, build, and optimize ETL pipelines and data workflows. Collaborate with teams to translate requirements into data solutions, ensuring data quality and efficiency.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

The Senior Data Engineer will design and implement data pipelines, optimize performance, ensure data quality, and collaborate with teams for analytics and reporting.

Intern will gain hands-on experience in data engineering by working with technologies like Databricks, SQL, and Azure. Responsibilities include technical proficiency development and participation in AI projects.

The Principal Data Engineer will lead the Data Resolution team, focusing on complex data challenges using Spark, system architecture, and mentorship, to optimize graph data pipelines.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

The Data Engineer will build and maintain data pipelines, optimize queries, collaborate with teams, and ensure data quality across Google Cloud systems.

The Lead Python Developer will design and develop Python-based analytics solutions, create reporting tools, collaborate with stakeholders, and mentor peers in a healthcare analytics environment.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

The Lead Data Consultant will design and deliver scalable data solutions, manage project teams, and optimize client data environments, ensuring effective project execution and communication.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

The Data Engineer will design and maintain scalable data infrastructure and pipelines, ensuring data quality and security on Azure, while collaborating with client teams.

Design, build, and maintain scalable data pipelines and tooling to support AI and analytics. Manage datastores (MySQL, Redshift, OpenSearch), implement AWS-based ETL/ELT solutions, collaborate with data science and product teams, and ensure data quality, automation, and production reliability.

The Data Engineer will build and maintain data pipelines, ensuring high-quality and scalable data systems while working on analytics and machine learning accessibility.

The Senior Data Analytics Engineer facilitates data-driven audit testing by integrating data sources, developing datasets, managing the AWS platform, and mentoring teams.

The Data Solutions Engineer will architect data systems, build pipelines, ensure data governance, optimize performance, and mentor team members to support analytics and operational workloads.

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

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 Data Solutions Engineer designs and operates scalable data pipelines using Azure tools, ensuring data quality and efficient analytics for BI Development.

Design, build, and operate resilient batch and streaming data pipelines. Own datasets end-to-end. Collaborate with data science teams and define data quality standards.

The Senior Data Engineer will develop and maintain scalable data pipelines, ensure quality standards, improve engineering practices, and engage with data teams to optimize operations.

The Lead Data Engineer will design and build scalable data pipelines on Databricks, develop ETL/ELT pipelines with PySpark, and mentor engineering teams for data transformation and analytics.

Design and develop scalable data pipelines using PySpark and Databricks, focusing on data ingestion, transformation, validation, and performance optimization.