Engineering Director, Knowledge Catalog

Within Google Cloud, Dataplex is the intelligent data fabric that enables organizations to unify, manage, and govern their data across lakes, warehouses, and data marts. Our mission is to transform data management from a manual, siloed process into a seamless, autonomous, and secure ecosystem that scales with the speed of modern business. This is being done by deeply infusing Generative AI into the Dataplex platform. This role is central to that mission, defining how we leverage Dataplex’s unique ability to automate data profiling, quality, and lineage to provide a trusted, self-healing, and AI-ready foundation for the entire data lifecycle.

As an Engineering Director, you will be an expert visionary and generalist, leading the technical and architectural reinvention of the Dataplex platform. You will define and drive the long-term strategic technical priorities for integrating AI as a core competency into our data governance and integration fabric.

In this role, you will be responsible for defining how Generative AI, automated metadata management, and our distributed data mesh architecture will evolve to create a truly autonomous, trusted, and AI-ready data ecosystem. You will employ a hybrid approach of direct technical guidance, mentorship, and risk/growth management. You will act as an escalation point for technical decisions across the team and function as a trusted partner for other C-level leaders in defining strategy and shaping our investment portfolio. Additionally, you will function as a trusted partner and key technical influencer both internally and externally.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Minimum qualifications:

• Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience
• 15 years of experience in software engineering or equivalent.
• 10 years of experience leading organizations of engineers.
  
  

Preferred qualifications:

• Expertise in AI/ML, including LLMs and vector embeddings, to automate data classification, sensitive data detection, and discovery in multi-cloud environments.
• Architectural expertise in cloud-native data platforms, with a focus on in-place platform modernization to natively support AI-driven, real-time data management experiences without disrupting existing enterprise workloads.
• Expertise in distributed data processing frameworks (e.g., Spark, Flink, BigQuery) and an intuition for how to build a scalable, real-time data foundation that provides high-quality, governed data to AI/ML models at latency and cost goals.