Data Engineer III
Robert Half
- San Ramon, California, United States
- Remote, Hybrid, Onsite
- $104,000 - $153,000 a year
- Posted Jun 15, 2026
Job description
About the role
The Data Engineer III will lead the architecture and development of complex data pipelines on the Databricks lakehouse for the ATI Data Science Innovation team, mentor engineers, ensure data quality and governance, and build infrastructure that supports AI/ML and Agentic AI workloads across the enterprise.
About the company
Robert Half is a Fortune‑listed staffing and professional services firm founded in 1948, with about 14,500 employees, offering specialized staffing, consulting, risk advisory and other business services; its ATI Data Science Innovation department drives AI‑focused initiatives.
Requirements
- Bachelor's in Computer Science, Engineering or related field (Master's preferred)
- 5+ years of Python and SQL experience in data engineering for big‑data ML/analytics workloads
- 5+ years designing, building and troubleshooting scalable ETL/ELT pipelines for production systems
- 3+ years with cloud data services (AWS), container orchestration (Docker, Kubernetes) and IaC (Terraform, CloudFormation)
- 3+ years architecting ML workflows and data platforms with CI/CD, automated testing and distributed processing (Spark)
- 3+ years collaborating cross‑functionally with Data Science, MLOps, Platform Engineering and DevOps teams
- 3+ years implementing data quality testing and optimizing SQL/Python for cost and performance in the cloud
- Understanding of the full Data Science SDLC and experience mentoring engineers
- 2+ years hands‑on experience with Databricks (Delta Lake, Unity Catalog, Databricks SQL)
- Experience with MLflow experiment tracking and model registry workflows
- Experience designing pipelines that serve AI/ML inference, including real‑time feature engineering and embedding generation for LLM‑based systems
- Understanding of data engineering support for Agentic AI, including low‑latency retrieval and autonomous workflow pipelines
- Familiarity with Databricks Mosaic AI, Vector Search or Feature Store
- FinOps awareness for compute cluster optimization and cost attribution
- Nice‑to‑have: familiarity with Salesforce/Heroku data infrastructures, data virtualization (e.g., Dremio), platform engineering concepts, migration from legacy warehouses to lakehouse, and Odaseva data security