Senior/Staff Data Engineer - Bangkok based, relocation provided

About Agoda

At Agoda, we bridge the world through travel. Our story began in 2005, when two lifelong friends and entrepreneurs, driven by their passion for travel, launched Agoda to make it easier for everyone to explore the world. Today, we are part of Booking Holdings [NASDAQ: BKNG], with a diverse team of over 7,000 people from 90 countries, working together in offices around the globe. Every day, we connect people to destinations and experiences, with our great deals across our millions of hotels and holiday properties, flights, and experiences worldwide.

No two days are the same at Agoda. Data and technology are at the heart of our culture, fueling our curiosity and innovation. If you’re ready to begin your best journey and help build travel for the world, join us.

Get To Know Our Team:

Join a small platform team building the next generation of natural-language analytics at Agoda. You will own the governed knowledge layer between our data warehouse and the applications, assistants, and analysts that consume it: metrics, domain context, quality signals, and the APIs that expose them. This is semantic modeling and knowledge engineering, not dashboard reporting or model training.

The Opportunity:

You will design and operate the shared knowledge layer behind conversational analytics at Agoda: turning scattered business knowledge into versioned, reviewable assets that return consistent, current answers. You pair with a senior tech lead on product architecture. Your scope is the layer itself (models, context, validation, governance workflows) and how it is exposed through service and agent interfaces.

What You’ll Do:

• Model governed metrics, dimensions, joins, and grain for business domains
• Structure expert context, glossaries, and validated question patterns as Git-reviewed assets
• Build freshness, drift, and validation signals; treat stale knowledge as a production issue
• Expose the layer through APIs so applications and agents get deterministic answers
• Enable domain owners to propose, review, and publish knowledge without engineering deploys
• Partner across analytics, product, and platform teams on access control and quality standards

Required Qualifications:

• 4+ years in BI, analytics engineering, data platform, or software engineering with heavy data modeling
• Semantic or metrics-layer experience (LookML, dbt metrics, or equivalent)
• Strong SQL and analytical modeling on OLAP / warehouse systems
• Knowledge engineering: versioned definitions, peer review, contributor workflows
• API / service mindset: you design for multiple consumers, not a single dashboard
• Clear communication across engineering, analytics, and product partners

Preferred Qualifications:

• Open-source semantic layer engines (Cube, MetricFlow patterns)
• Agent or tool-interface experience for LLM-powered applications
• NL-to-data products, retrieval, or evaluation harnesses
• Python or Node.js service development
• Large-scale e-commerce or travel analytics environments