Serve as the primary technical partner to customer Chief Technology Officers (CTOs) and chief architects, leading high-stakes workshops to align their technology roadmap with Google’s Data and AI strategy.
Act as principal architect for Google’s Agentic Data Cloud, setting engineering standards, winning complex workloads, and accelerating adoption across lighthouse engagements from evaluation through ramp.
Capture repeatable patterns to build scalable Golden Path blueprints, reference architectures, and field-ready assets that drive broad adoption across thousands of customers.
Synthesize customer friction points and cross-product gaps into authoritative engineering requirements to shape and protect Google's unified Data and AI narrative.
Mentor and guide subregional Datacloud Customer Engineers, remaining experience for critical architectures while directing the team to deliver secure, scalable solutions.
Minimum qualifications
Bachelor's degree or equivalent practical experience.
10 years of experience with delivering data and analytics solutions in a customer-facing or support role.
Experience in building and deploying enterprise Generative AI applications and agentic workflows integrated with modern data platforms.
Experience in architecting distributed data systems (e.g., Lakehouse, Data Mesh), batch and streaming pipelines (e.g., Spark, Flink, BigQuery, Iceberg), and transactional databases (e.g., PostgreSQL, MySQL, NoSQL databases).
Experience in cloud-native data warehousing, ETL/ELT pipelines, real-time event streaming, governance, and end-to-end migrations.
Experience partnering with, influencing, and presenting architectures to executive leaders and technical stakeholders.
Preferred qualifications
Experience interacting with C-level executives (e.g., CTO, Chief Data Officer (CDO), Chief Information Officer (CIO)) to translate complex business requirements into technical strategies via simple, compelling narratives including organizational, communication, and leadership skills, with the ability to prioritize and manage across multiple time sensitive initiatives.
Knowledge of emerging agentic technologies and their implications for data architecture.
Proficiency in writing, refactoring, and debugging Python, PySpark, and SQL natively to support high-performance data engineering, ML frameworks (e.g., PyTorch, TensorFlow), and troubleshooting workloads.
Technical expertise in designing distributed systems, data platforms, and AI/ML architectures for enterprise scale.
Bachelor's degree or equivalent practical experience.
10 years of experience with delivering data and analytics solutions in a customer-facing or support role.
Experience in building and deploying enterprise Generative AI applications and agentic workflows integrated with modern data platforms.
Experience in architecting distributed data systems (e.g., Lakehouse, Data Mesh), batch and streaming pipelines (e.g., Spark, Flink, BigQuery, Iceberg), and transactional databases (e.g., PostgreSQL, MySQL, NoSQL databases).
Experience in cloud-native data warehousing, ETL/ELT pipelines, real-time event streaming, governance, and end-to-end migrations.
Experience partnering with, influencing, and presenting architectures to executive leaders and technical stakeholders.
