Head of Data, Customer Experience

Adyen•San Francisco, CA

8d•$258,500 - $367,000•Onsite

About The Position

Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Adyen seeks a technical leader for its Customer & Developer Experience org, focused on end-to-end data strategy, instrumentation, tooling, and analytics. You will lead a team of data engineers and analysts to own the architecture, quality, and governance of data, ensuring it is ready for advanced analytical and machine learning applications. This is a high-impact management role with a dual mandate: to define the data strategy and to build the ML/AI foundations necessary for future customer experiences. Your team will partner with Product to identify, design, and measure KPIs to track business performance. Your team will generate insights for the business, and over time provide self-serve tooling to empower non-technical functions. Additionally, your team will be the enabler of new AI-powered product experiences that optimize key workflows and extend capabilities to customers via natural language. Your data strategy will create a foundation for high quality, accessible, and semantic understanding of our data.

Requirements

10+ years of experience leading technical data, analytics, or data engineering teams, ideally focused on high-volume, real-time product data and ML readiness.
Proven ability to bring structure and clarity to complex, decentralized data environments.
Expert-level technical foundation, including fluency in SQL, Python (or similar scripting languages), and hands-on experience designing and deploying data pipelines and ML-ready feature stores.
Proven experience working hands-on with AI/ML products, including understanding model deployment, serving infrastructure, and the data lifecycle needed to power agentic customer experiences.
Demonstrated ability to connect technical data strategy (architecture, governance, platform) directly to business outcomes and product prioritization.
Comfortable operating in global, matrixed organizations where influence, alignment, and judgment are critical.

Nice To Haves

Builder & Unifier: You enjoy turning fragmented systems into cohesive, scalable foundations.
AI Native: You have incorporated AI tools and thinking into your daily professional life, and are excited for its ability to improve outcomes for your team and customers.
Results-Driven: You focus on outcomes, learning velocity, and measurable impact—not vanity metrics.
Clear Communicator: You translate complexity into clarity and align diverse stakeholders around shared goals.

Responsibilities

Architect a data strategy. Design and lead the implementation of a foundation for all customer and product data, explicitly integrating ML-ready feature stores and real-time experimentation systems.
Establish definitions of success. Partner with Product to define and standardize clear, consistent success metrics, KPIs, and funnels across product teams to ensure all business and product decisions are driven by a shared, unambiguous understanding of performance and customer outcomes.
Drive strategic influence. Transform the data platform into a key strategic asset, empowering teams with rigorous, actionable insights that directly inform product roadmap prioritization, investment decisions, and long-term business strategy.
Scale High-Velocity Experimentation Infrastructure. Significantly increase the rigor, consistency, and speed of product testing by designing and operationalizing scalable A/B testing and experimentation platforms.
Ensure Data Integrity and Enterprise Governance. Own and implement the technical standards, tooling, and governance processes necessary to maintain trusted, high-quality, and compliant data for all analytical and ML applications.
Accelerate AI/ML Product Readiness. Drastically reduce the time-to-market for new AI-powered product features by leading the effort to establish high data consistency, semantic understanding, and the necessary feature engineering pipelines to unlock future agentic customer experiences.
Define and champion Adyen’s CX data strategy, setting the long-term vision for data architecture, real-time instrumentation, and ML-ready feature stores.
Drive technical alignment and execution with core Data Engineering teams to build robust, low-latency, and high-quality data pipelines, ensuring enterprise-grade governance.
Partner with Product, Data Engineering, and Commercial teams to align technical platform strategy and metrics directly with key business outcomes.
Design, build, and scale the experimentation platform infrastructure (A/B testing, multi-arm bandit), ensuring rigorous statistical methods and high-velocity iteration for product teams.
Define standardized, clear metrics to measure product performance, adoption, and customer impact across the entire customer lifecycle.
Translate complex data systems, platform architecture, and AI/ML readiness into clear, executive-ready recommendations that influence product roadmaps, prioritization, and business investment decisions.
Act as a trusted, technical partner to senior leaders, providing executive-ready insights that connect platform health and customer behavior to strategic outcomes.
Drive cross-functional alignment by unifying teams around shared, consistent metrics, data strategy, preventing fragmentation of definitions, dashboards, and reporting.
Lead, coach, and grow a high-performing team of embedded data engineers and analysts across the organization.
Establish best-in-class expectations for data engineering standards, analytical rigor, strategic storytelling, and measurable business impact.