Staff, Data Engineer

What you'll do

• Build and operate shared context, semantic, metadata, and data-platform capabilities for humans and digital workers.
• Define standards and operating practices for agent-generated pipelines, AI data lifecycle, governance, quality, and infrastructure.
• Lead technical direction, resolve incidents, and develop senior engineers across Enterprise AI.

What they ask for

• Bachelor's degree in Computer Science, Data Engineering, or a related field, or equivalent practical experience.
• 10+ years of data engineering experience, including defining data architecture for production AI or machine learning systems.
• Experience building AI data infrastructure, context or semantic infrastructure, and agent- or LLM-powered systems within data workflows.
• Experience setting AI data quality, lineage, governance, cloud platform, and infrastructure-as-code standards.

Nice to have

• Prior Staff, Principal, or equivalent experience operating a data platform for AI; regulated-industry experience is also a plus.
• Public technical contributions or experience defining enterprise AI pipeline architecture standards.

Tools

Model Context Protocol (MCP), feature stores, vector databases, streaming data pipelines, evaluation datasets, cloud data platforms, infrastructure as code, Databricks Unified Data Analytics Platform

AI focus

Builds the governed context layer, data infrastructure, governance, and operating standards that enable AI systems and digital workers to use and operate data pipelines in production.