Define and drive the vision, strategy, and measurable outcomes for complex technical programs across the Data, Platform and Tools space. Drive improvements in performance, reliability, scalability, capacity, cost efficiency, and operational readiness of high-scale production systems. Develop end-to-end understanding across services and online/offline components by reviewing architecture and code paths, analyzing production data and logs, evaluating experiments, and testing hypotheses. Form an evidence-based technical point of view, surface trade-offs, and influence engineering and product direction through depth, data, and sound judgment. Own complex initiatives end to end — from problem framing and solution definition through alignment, execution, rollout, measurement, and course correction — with accountability for outcomes. Define success criteria, metrics, and guardrails; use experimentation, telemetry, and production signals to assess customer, system, and business impact. Align engineering, product, data science, and business stakeholders across organizational boundaries, including senior leaders and globally distributed teams. Identify and resolve technical, organizational, and execution dependencies and risks; navigate ambiguity and obstacles without losing momentum or lowering the quality bar. Communicate program state early and clearly, including progress, risks, misses, trade-offs, and decisions, to both technical and executive audiences. Identify systemic opportunities beyond individual initiatives, connect investments across technical areas, and help shape broader technical and program direction. Use AI effectively for research, analysis, debugging, system understanding, and program execution while applying appropriate technical judgment and validation. Champion a culture of rigor, curiosity, quality, inclusion, and truth-seeking, with decisions grounded in evidence and systems designed for long-term robustness and scale. Bachelor's Degree AND 6+ years experience in engineering, product/technical program management, data analysis, or product development OR equivalent experience. Experience building, operating, optimizing, or troubleshooting high-scale ads serving, recommendation, machine learning, data, experimentation, or other distributed systems. Understanding of ads technology and how serving, inference, experimentation, data, identity/privacy, and monetization systems work together. Experience improving the performance, reliability, capacity, or cost efficiency of compute-intensive systems; CPU/GPU inference experience is a plus. Ability to work hands-on with code, queries, logs, dashboards, experiments, and system documentation to investigate complex behavior and develop an independent technical point of view. Demonstrated versatility across technical domains and ability to adapt as business and engineering priorities evolve, including effective use of AI tools for technical research, analysis, debugging, and productivity. Experience driving cross-team change, leading programs with significant customer or business impact, and mentoring TPMs, engineers, or other technical leaders.