Top Hybrid Data Engineering Jobs

The Analytics Engineer will build and maintain data pipelines, develop data models, and ensure data accuracy for analytics and reporting.

The AV Safety Analytics Engineer will develop data analytics infrastructure for automated vehicle safety, utilizing cloud processing and statistical methods. Responsibilities include creating data visualizations, monitoring metrics, and ensuring data integrity across systems.

As a Staff Data Engineer, lead the technical roadmap for data infrastructure, manage complex data architectures, and mentor engineers while driving the organization's data platform innovations.

The Data Engineer will build datasets and pipelines for marketing and sales reporting, collaborating with various teams to support data needs and ensure scalable solutions.

The Cloud Data Architect will design and implement data architecture solutions within a SaaS environment, ensuring data security and efficiency while leading technical standards and collaborative teams.

Join Chewy as a Data Engineer I to learn and build ingestion and transformation pipelines, write SQL/Python for data prep, apply dbt Cloud modeling, monitor data quality, assist in triage, and collaborate with analytics and data science under senior mentorship while gaining exposure to Snowflake, Kafka, AWS, Terraform, and data observability.

As a Senior Data Engineer, you will design, develop, and implement data solutions, utilizing cloud technologies and collaborating with teams to support technical solutions and mentor others.

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

Assist in developing, testing, and maintaining software applications, collaborate on designs, participate in code reviews, and contribute to documenting processes.

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.