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Robert Half, one of FORTUNE’s World’s Most Admired Companies and a Fortune 100 Best Companies to Work For is hiring for a Data Engineer III to join the ATI Data Science Innovation department.
Job Responsibility
Lead architecture and design of complex data pipelines on Databricks lakehouse architecture (Unity Catalog, Delta Lake, Structured Streaming)
Define technical approach for data engineering initiatives, mentor less-senior engineers, and set standards for code quality through leadership and code reviews
Design and build data foundations that enable AI/ML capabilities — feature stores, embedding pipelines, vector search indexes, and model training datasets
Align data engineering solutions with business strategy, including support for Agentic AI workloads
Own health, scalability, and modernization of data infrastructure with Databricks as the strategic platform — including workload migration, compute optimization, and Unity Catalog adoption
Optimize pipeline performance (Delta Lake table layouts, clustering, Z-ordering) and establish monitoring/alerting best practices with clear SLAs
Build data infrastructure supporting Agentic AI systems — real-time data access layers, context retrieval pipelines, and agent-accessible data services
Collaborate cross-functionally with DevOps, Platform Engineering, and MLOps roles to integrate data solutions into the broader technology environment and shared AI infrastructure – Mlflow registries, feature stores, and agent orchestration layers
Provide consultation to Senior Leadership on complex projects and drive continuous improvement initiatives
Champion data governance at all layers for data, models, and AI assets
Implement data quality strategies (master data management, validation rules, Delta Live Tables expectations) to ensure trust in enterprise data
Serve as liaison across data engineering, AI engineering, and business teams
promote data literacy and stewardship
Requirements
Bachelor's in Computer Science, Engineering, or related field (Master's preferred)
5+ years with Python and SQL in data engineering for big data ML/analytics workloads
5+ years designing, building, and troubleshooting scalable ETL/ELT pipelines for business-critical 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/performance in the cloud
Understanding of the full Data Science SDLC, and experience mentoring engineers
Strongly Preferred - 2+ years hands-on 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 — real-time feature engineering, embedding generation, and context retrieval for LLM-based systems
Understanding of how data engineering supports Agentic AI: agent-accessible data services, low-latency retrieval, and pipelines enabling autonomous multi-step workflows
Familiarity with Databricks Mosaic AI, Vector Search, and/or Feature Store
FinOps awareness — compute cluster optimization, cost attribution by workload
Nice to have
Familiarity with Salesforce/Heroku data infrastructures
Experience with data virtualization (e.g., Dremio)
Understanding of Platform Engineering concepts and internal developer platforms
Experience migrating from legacy data warehouse/lake to unified lakehouse architecture
Familiarity with Odaseva data security and management