Data Products & AI Architect - Enterprise Applications, FinTech

About us: At Booking.com, data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. We’re the thrill of the first night in a new place. The excitement of the next morning. The friends you make. The journeys you take. The sights you see. And the food you sample. Through our products, partners and people, we can empower everyone to experience the world. FinTech business unit – with teams in Amsterdam, Bangalore, London and Shanghai – develops new products and drives innovation to help us continue removing financial friction from the travel process. That makes buying and selling travel-related products and services simpler for both travelers and partners. The newly established Data & AI organisation within FinTech: Financial Systems/Enterprise Applications brings together Enterprise Data Products, AI First Finance (Finance Intelligence & Autonomous Finance), and Intelligent Automation into a single, unified organization. To support this vision, the Architecture team requires a strong technical lead for the Data and Product AI organisation to partner across trusted Finance intelligence, governed agent workflows, and scalable data foundations, establishing clear architecture responsibility while collaborating closely with Product and Engineering on delivery and trade-offs. Role Description AI First Finance is transforming how Finance teams across Booking Holdings access information, make decisions and execute work. We are developing trusted Finance intelligence and governed AI workflows, connecting certified financial data with reusable enterprise capabilities. As our Data & AI Architect, you will lead technical design for these capabilities and work hands-on with engineering teams to bring them into production. You will connect data products, semantic models, enterprise systems and AI services into solutions that are reliable, traceable and practical to operate. Your designs will support shared capabilities across brands while respecting local business definitions, systems and data-access boundaries. You will partner closely with the AI First Finance Product leaders, engineering leaders, Finance experts, enterprise data teams and Central Technology. Together, you will translate prioritized business problems into achievable delivery increments, making trade-offs between speed, quality, cost and risk explicit. You will validate critical decisions through working implementations and production evidence, and develop reusable patterns that help subsequent teams deliver faster. Key Job Responsibilities • Partner with Product, Engineering, Finance, Data Governance, Legal, Security, and Compliance to translate business priorities and governance policies into scalable architecture, balancing value, delivery speed, cost, and risk. • Connect AI products to certified Finance data with clear definitions, data contracts, quality standards, lineage, and access controls. • Design Finance intelligence with governed calculations, traceable answers, and evaluation against examples validated by Finance. • Design and deploy AI workflows with secure integrations, robust SAP and third-party application connections, appropriate permissions, human approvals, and safe failure handling. • Reuse, extend, and evaluate enterprise capabilities, making clear build, buy, and reuse decisions in collaboration with Central Technology. • Validate architecture through prototypes, reference implementations, code reviews, process automation, and hands-on support for production delivery. • Embed evaluation, monitoring, audit evidence, SOX compliance, and segregation of duties (SoD) risk controls throughout the AI lifecycle alongside Engineering, Security, and Risk & Controls. • Architect robust compliance structures and reusable patterns across teams and brands that scale with company growth while respecting local business definitions and data boundaries. • Measure quality, reliability, performance, cost, and reuse to guide continuous improvements with Product and Engineering. Qualifications • 10+ years in data engineering or data architecture, with at least 3–5 years in a lead or architect role owning platform design for large organisations • Delivered at least one enterprise data or AI platform into supported production (with SLAs, on-call and real internal users), using tools like Snowflake, Databricks, BigQuery, Spark, Kafka or Airflow. For example, you've migrated a legacy warehouse to a modern lakehouse, or launched a self-serve data platform used by multiple teams • Experience building AI applications or agents that run in production, not just prototypes. You've worked with LLM APIs (e.g. OpenAI, Anthropic, AWS Bedrock, Vertex AI) and frameworks like LangChain, LangGraph or MCP • Hands-on data architecture skills across semantic modelling (e.g. dbt, LookML, Cube), data contracts, data quality (e.g. Great Expectations, Soda, Monte Carlo), lineage (e.g. OpenLineage, DataHub, Collibra, Unity Catalog) and access controls (RBAC/ABAC, row- and column-level security). • Designed systems that run at high volume, for example processing billions of events or terabytes of data per day • Practical experience with security, privacy, governance and audit requirements for enterprise data and AI, such as GDPR, SOX, PCI DSS, ISO 27001 or the EU AI Act. You've built in controls like encryption, PII masking, audit logging and data retention, and supported internal or external audits • A track record of working with Product, Engineering and Finance stakeholders. You can point to times you turned unclear requirements into a clear technical plan, explained trade-offs (cost vs. speed vs. risk) to non-technical audiences, and got teams aligned on a decision, for example through architecture decision records (ADRs) or design reviews. Nice to have • Experience in FinTech, payments or other regulated industries • Experience with infrastruct