Work closely with our strategic customers to understand their complex data silos and co-develop our AI-first data management roadmap.
Partner with C-level leaders to define the strategy for the next generation of data fabrics, ensuring Knowledge Catalog is the backbone of Google Cloud’s data strategy. Build product and UX relationships and collaborate with SRE, Privacy, Security, and Compliance teams.
Influence thought leaders across Google Cloud (e.g., Vertex AI, BigQuery) and Google (e.g. DeepMind) to align roadmaps, ensuring the data fueling Google’s AI models is governed, high-quality, and seamlessly accessible.
Lead Knowledge Catalog’s AI-first long-term strategy, defining roadmaps for autonomous governance, AI metadata discovery, and self-healing pipelines while maintaining technical excellence.
Manage complex tradeoffs such as LLM integration (i.e. rewrite vs. encapsulate), balancing AI automation with manual controls, and transitioning between centralized and data mesh architectures.
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.
