Top Data Engineer Jobs in Houston, TX

As an Azure Data Engineer, you'll design and maintain data solutions on Azure, collaborate with teams, optimize ETL/ELT processes, and ensure data governance.

Design, implement, and optimize ETL pipelines, ensuring data quality and flow, while collaborating with stakeholders in healthcare technology.

The Data Engineer will design and maintain ETL pipelines, optimize data models, develop exporters, and ensure reliable data services for the Lambda app in a fast-paced startup environment.

The Data Engineer designs and builds cloud-native data platforms, collaborates with clients to deliver scalable solutions, and leads technical sessions and roadmaps.

As a Founding AI/Data Engineer, you'll design and build AI-driven products, collaborate across teams, and ensure system reliability while enhancing user experiences.

As a Snowflake Data Engineer, you'll design and manage data pipelines, optimize performance in Snowflake, and collaborate across teams to provide analytics solutions.

As a Principal Data Engineer, you will optimize data systems, design scalable pipelines, and establish data governance, collaborating closely with teams to enhance Yahoo News's data architecture.

The role involves designing and building scalable APIs and microservices, collaborating with teams to align technical work with business goals, and driving operational excellence in data engineering.

As a Data Engineer at Thatch, you'll develop data infrastructure, build ELT pipelines, and collaborate across teams to provide analytics insights.

As a Lead Infrastructure Engineer, you will deliver infrastructure excellence, drive projects, ensure performance, and implement creative solutions to enhance technology processes.

The Data Platform Engineer will improve data capture tools, enhance pipeline frameworks, and ensure data observability for Upstart's data lakehouse.

Lead the technical vision for the Data Platform team, focusing on building scalable infrastructure and providing mentorship across engineering teams.

Design and develop reliable distributed data systems to deliver high-quality data for Netflix's product, collaborating with data scientists and engineers.

The Data Engineer will build scalable privacy-centric data frameworks, engineer data pipelines, and ensure compliance with privacy regulations at Netflix.

The Data Engineer will architect and expand data products for Netflix's Ads business, developing data pipelines, collaborating with analysts, and ensuring data quality.

The Data Engineer will build data infrastructure, develop CI/CD pipelines, and create solutions for data engineering challenges, requiring strong technical skills and collaboration.

The Data Engineer will manage data ingestion in Salesforce, standardize lead data, ensure data quality, and optimize integration workflows.

The MLOps/Data Engineer will design and implement a data and ML platform, build data pipelines, and ensure reliability and scalability of ML models, collaborating closely with data science and product teams.

Lead data engineering efforts by gathering requirements, building scalable data platforms, integrating systems, and delivering high-quality code to enhance decision-making through data.

The ML Data Engineer will manage the entire data lifecycle for ML and CV development, evaluate ML models, and develop CI/CD for ML data pipelines.

Lead the design and development of scalable data solutions and architectures, collaborating with cross-functional teams and mentoring junior members.

The Data Engineer will build scalable data pipelines, develop data warehouse solutions, and define data access standards while managing data integrity and access patterns.

As a Senior Data Engineer, you will build and scale data pipelines, ensure data quality, and collaborate to enhance the data infrastructure at Perchwell.

Design and build data integration frameworks and pipelines while ensuring data accuracy and resolving management issues, collaborating with DevOps and data scientists.

The Data Engineer II will develop and maintain data pipelines, optimize workflows, troubleshoot issues, and implement best practices while collaborating with cross-functional teams.