Senior BI Analyst (Supply Analytics, Bangkok-based)

About the role

The role translates internal briefs into analytical projects to refine scope and define hypotheses for supply analytics.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Growth opportunities within supply and the wider business are identified proactively to support top and bottom-line objectives.

New analytical initiatives are driven to improve organizational efficiency and shape Agoda supply strategies.

Projects that scale the Supply organization's use of data, insights, and intelligence are identified, supported, and led.

Manual operational processes are automated, and time savings from modernized operations are reported back.

Requirements

4+ years of experience in analytics, data science, insights, or strategy is required for this senior role.

A Bachelor's degree is required in a business or quantitative subject such as computer science, mathematics, engineering, science, economics, or finance.

3+ years of hands-on experience with BI and analytics tools, including SQL, Tableau, Metabase, or PowerBI, is required.

A hacker's mindset enables building simple but clever and elegant solutions under resource, operational, and time constraints.

Strong stakeholder management skills are required to present confidently to senior leadership and C-suite audiences.

Extreme comfort in ambiguous, fast-paced environments is necessary to operate effectively in this role.

The ability to multi-task, prioritize, and coordinate resources is required to manage shifting demands.

Nice to have

Travel industry, e-commerce, tech, or consulting experience is valued for this position.

Experience conducting A/B testing experimentation adds to the profile.

Python or R experience provides an advantage for data work.

Knowledge of statistical modeling or machine learning techniques, such as regression, logistic regression, or random forest, is a plus.

Skills & tools

The role relies on SQL, Tableau, Metabase, or PowerBI for data analysis and storytelling.

Python or R may be used for advanced analytics and automation.

Statistical modeling supports data-driven recommendations.

Practical notes

This position is based in Bangkok, Thailand, with relocation provided.

The role is not open for remote working.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

This role focuses on supply analytics to drive growth and efficiency for accommodation partners.

The team operates as creative entrepreneurs developing data-driven solutions.

The position sits within a fast-growing, dynamic supply department.

Tools commonly used include SQL, Tableau, Metabase, PowerBI, and optionally Python or R.

Analytical work supports decision-making, performance measurement, and business recommendations.

The environment requires comfort with ambiguity, fast-paced execution, and strong communication.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.