Alongside leadership responsibilities, I continued to work deeply on hard technical problems in Cloud Spanner spanning scalability, distributed resource management, customer-facing architecture, and database infrastructure.

• Designed distributed rate capping and token-bucket-based resource management in Cloud Spanner at very large scale.
• Led technical direction for Cassandra compatibility work in Spanner, including ideation, prototyping, implementation, and technical problem solving.
• Unblocked scalability bottlenecks in large Spanner regions and improved system behavior at scale.
• Worked directly on database and distributed systems problems involving scale, performance, resource management, and architecture.
• Partnered with customers and engineering teams to move complex technical efforts from proof-of-concept to committed adoption.

I led teams spanning software engineers and technical leads across key Cloud Spanner initiatives, helping move projects from early concept through execution and delivery.

• Led teams working on major Cloud Spanner efforts, including customer migrations and product initiatives.
• Worked closely with Directors, UTLs, PMs, and senior engineering leadership to align scope, priorities, and execution plans.
• Helped drive ambiguous, cross-functional efforts to completion through alignment, communication, dependency management, and conflict resolution.
• Balanced strategic priorities, engineering execution, and customer needs across internal and external stakeholders.
• Supported projects from early technical exploration through committed delivery, helping teams stay focused on high-impact outcomes.

• Worked on fleet-wide profiling and AutoFDO infrastructure.
• Improved profile generation, stack unwinding accuracy, and large-scale optimization workflows.
• Designed context-sensitive profiling systems delivering major compute and memory savings.
• Improved rollout and recovery performance in production systems.

• Part of the team that designed and implemented the JIT shipped in MATLAB R2015b.
• Built compiler and interpreter optimizations while preserving language semantics.
• Created tools to analyze MATLAB code and guide optimization strategy.