Top Hybrid Data Engineering Jobs

The Associate Data Engineering role focuses on building data solutions and pipelines, learning from senior engineers, and participating in a structured tech program.

Design, build, and maintain cloud-based data pipelines and products (batch and streaming). Develop microservices, orchestration, and feature stores in AWS. Partner with stakeholders, automate ETL, enable self-service analytics, and mentor junior engineers.

The role involves driving data engineering innovation, mentoring, promoting modern technologies, and enhancing technical practices at Capital One. Distinguished Engineers will influence key stakeholders while building next-generation solutions and talent within the organization.

As a Staff Data Engineer, you develop data products, design data for analytics, and support Precision Ag business through data insights and tools. You will harmonize data, implement data pipelines, and lead teams on complex projects, ensuring efficient and impactful solutions for agriculture.

Lead development of scalable data solutions that integrate deep learning. Manage data quality, metadata, and facilitate cross-functional data initiatives for business value.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

The Senior Snowflake Data Engineer designs and optimizes Snowflake architecture and pipelines, ensuring data governance and performance across Marketing and Sales teams.

The Senior Data Engineer will build and optimize data pipelines, ensure data integrity, and collaborate with stakeholders to meet business requirements while implementing best coding practices.

Develop and implement advanced algorithms, apply statistical techniques, collaborate with clients, assess technologies, and mentor team members.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.