[BI] Senior BI Developer (Analytics Engineer)

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.

THE OPPORTUNITY

As a Senior BI Developer, you will be a key individual contributor building trusted data products for Agoda’s technology organization, including curated datasets, metrics, dashboards and visualizations, and scalable data transformations. You will work closely with engineers, engineering managers, team leads, and senior leadership to help the organization make better decisions with data. This role combines analytics, metrics definition, and stakeholder consulting with hands-on data engineering and development

IN THIS ROLE, YOU’LL GET TO

• Act as a partner and consultant to engineering managers and leaders: translate stakeholder and engineering questions into measurable metrics/information such as developer velocity, cost efficiency, and code quality, and keep those definitions consistent across reports.
• Design, build, and maintain ETL/ELT transformations, including Spark/Python workflows and warehouse models, so data is accurate, timely, tested, and fit for reporting. As a Senior, review others’ pipeline and metric work, raise delivery standards, and unblock the team.
• Analyze large datasets with SQL and Python to support engineering and operational decisions.
• Design and maintain dashboards and visual reports so engineering managers and leaders can explore metrics (for example developer velocity, cost efficiency, and code quality) without a ticket for every chart.
• Improve data reliability through validation, quality checks, observability, production troubleshooting, and query performance tuning. Own published metrics: explain filters and grain, flag numbers that should not be trusted, and recommend next steps.
• Use AI tools (for example Cursor, Claude, or similar) to accelerate SQL development, testing, refactoring, analysis, pipeline work, and documentation, and reject unsafe or unverified output before it reaches production or stakeholders.

WHAT YOU’LL NEED TO SUCCEED

• 5+ years in business intelligence, analytics, and/or data engineering, with experience in both stakeholder consulting and hands-on pipeline or data-product work.
• Advanced SQL (joins, window functions, and performance on large datasets).
• Strong Python skills (or similar) for building data pipelines.
• Experience with ETL/ELT and orchestration (for example Kubeflow or similar).
• Strong analytical thinking: comfortable framing business questions, exploring data, and turning results into clear insights.
• Experience building dashboards or visual reports for stakeholders.
• Ability to switch context between stakeholder discussions and hands-on technical work — metrics definition/analysis one day, ETL or data modeling the next.
• Practical AI-assisted development: generate, review, test, and own the result. You can explain what you trusted AI to draft and what you verified yourself.
• Clear written and verbal English, and the ability to work independently with engineers and leaders in an agile environment.

IT’S GREAT IF YOU HAVE

• Experience with software-delivery practices such as Agile/Scrum and CI/CD (GitHub Actions, GitLab CI, or similar).
• Interest in developer productivity or engineering analytics.
• Experience with common data platforms / big data technology (e.g., BigQuery, Snowflake, Redshift, Hive, Spark, Vertica, StarRocks, or similar).
• Hands-on experience with visualization or data-serving UIs such as Apache Superset, Tableau, or a lightweight web app.
• Experience building APIs or internal data products beyond dashboards, not only ad-hoc analysis.
• A degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.