• Lead engineering execution by clarifying priorities, ownership, timelines, risks, dependencies, and quality expectations. • Manage engineers and contractors, including coaching, feedback, prioritization, performance support, and team development. • Stay close to the codebase by contributing code, reviewing technical work, evaluating tradeoffs, and helping the team maintain momentum and quality. • Partner with technical and product leadership to turn product direction, AI strategy, user experience goals, and technical priorities into coordinated engineering plans. • Establish planning, review, release, and follow-through practices that improve predictability and reduce bottlenecks. • Maintain high standards for code quality, testing, documentation, reliability, security awareness, maintainability, and release readiness. • Build strong product and architectural context across AI-enabled workflows, integrations, local-first or customer-controlled architectures, and release requirements. • Partner across product, design, enterprise-facing teams, and technical leadership to balance speed, user experience, privacy, quality, enterprise readiness, and long-term sustainability. • Lead hiring, onboarding, and resource planning as the Thunderbolt engineering team grows. • Help Thunderbolt scale from an incubation-stage product into a reliable, enterprise-ready open-source AI product.

• Drive platform strategy and architecture by defining the technical roadmap and long-term vision for the Enterprise Data Platform utilizing Snowflake, dbt, Matillion DPC, and Streamlit • Balance platform evolution and business support, transitioning teams toward a modern platform operating model with medallion and mesh architectures while supporting critical organization-wide data initiatives • Embed AI into data and analytics by designing pipelines, analytics models, and Retrieval-Augmented Generation systems to transition central data and federated BI teams into AI-ready units • Enable self-service and mesh architecture to empower decentralized business functions to independently build custom data models across foundational analytics layers • Implement robust platform governance, observability, and cost attribution frameworks while executing hands-on technical troubleshooting, prototyping advanced workflows, and developing AI/LLM-powered AIOps systems for operational efficiency

• Lead, coach, and develop a team of Remote Service Engineers across multiple skill levels (Associate → Master) • Build a sustainable talent pipeline (entry-level growth, internal promotion, expert retention) • Drive employee engagement, retention, and career progression • Establish clear performance expectations and conduct regular coaching, case reviews, and development planning • Model Agilent Leadership Expectations , fostering a culture of accountability, ownership, and continuous improvement • Ensure timely and effective remote resolution of hardware, software, and application issues • Drive improvements in case resolution time, first-time fix rates, customer satisfaction / CX feedback • Ensure consistent, high-quality customer interactions across phone, email, and digital channels • Partner with Field Service and Scheduling teams to ensure seamless customer handoffs • Oversee support for Agilent’s instrument and software portfolio, including Analytical instrumentation platforms (e.g., LC, GC, spectroscopy, cell analysis, etc.), associated software platforms, applications, and workflows, • Ensure team capability across hardware troubleshooting, software diagnostics, scientific application support • Drive knowledge management (IKB, documentation, standard solutions) • Own team performance against key KPIs • Ensure consistent CRM usage and case documentation standards • Identify trends and implement corrective actions to improve performance • Support revenue and commercial success • Drive process improvement and transformation • Ensure cross functional collaboration

• Lead engineering execution by clarifying priorities, ownership, timelines, risks, dependencies, and quality expectations. • Manage engineers and contractors, including coaching, feedback, prioritization, performance support, and team development. • Stay close to the codebase by contributing code, reviewing technical work, evaluating tradeoffs, and helping the team maintain momentum and quality. • Partner with technical and product leadership to turn product direction, AI strategy, user experience goals, and technical priorities into coordinated engineering plans. • Establish planning, review, release, and follow-through practices that improve predictability and reduce bottlenecks. • Maintain high standards for code quality, testing, documentation, reliability, security awareness, maintainability, and release readiness. • Build strong product and architectural context across AI-enabled workflows, integrations, local-first or customer-controlled architectures, and release requirements. • Partner across product, design, enterprise-facing teams, and technical leadership to balance speed, user experience, privacy, quality, enterprise readiness, and long-term sustainability. • Lead hiring, onboarding, and resource planning as the Thunderbolt engineering team grows. • Help Thunderbolt scale from an incubation-stage product into a reliable, enterprise-ready open-source AI product.