Top Hybrid Data Engineering Jobs

Lead enterprise-scale data architecture and end-to-end solution design for Cat Digital. Create interoperable data products, design system-to-system data flows and data models, evaluate and deploy data platform solutions, and collaborate with engineering and product teams to accelerate time-to-data-value.

The SOX Data Analytics & AI Manager will develop and implement data analytics and automation solutions for SOX compliance, lead a team, and collaborate across functions to enhance efficiency in internal controls.

This role involves designing data architecture and solutions, managing data pipelines, leading POCs, and collaborating across departments to implement data governance and quality practices.

Design and maintain scalable data pipelines, collaborate with cross-functional teams, manage data architectures, optimize Snowflake performance, and enhance data workflows.

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.

Lead design, implementation, automation, and maintenance of large-scale ETL pipelines; optimize SQL and database performance; translate business needs into data requirements; enforce best practices (version control, code reviews, validation); mentor junior engineers; deliver timely, robust data solutions for global clients.

Lead analytics work for card products by building data solutions, dashboards, and data quality tooling. Partner with business stakeholders to translate needs into insights, leverage Snowflake and scripting (Python/R/Spark/SQL), and enforce data governance and access controls.

Design and build data solutions using open-source and cloud technologies; partner with business to create dashboards and analyses; ensure data quality, metadata, lineage, governance, and secure data access; develop tools for monitoring and enable self-service data capabilities.

Build and own end-to-end sales data pipelines and curated marts in Snowflake/dbt; implement monitoring, tests, and governance; run reverse-ETL to operational systems; partner with Sales, Ops, Finance, and People to deliver production-ready analytics products.

Lead and grow a data engineering team that designs, builds, tests, and operates large-scale data pipelines and warehouses. Partner cross-functionally to translate requirements, ensure data quality, observability, SLAs, security, and governance, and drive automation and performance for analytics, ML, and product use cases.

Lead design, build, and maintain scalable data pipelines and warehouses. Drive technical roadmap, mentor engineers, implement data quality/testing/monitoring, optimize performance and cost, and collaborate cross-functionally to ensure reliable data for BI, ML, and applications.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.