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Agoda

Senior Staff Backend Engineer

Bangkok, Thailand
C++ StarRocks ClickHouse Presto Trino Druid Pinot Doris Impala Kubernetes Docker Terraform Helm Ansible Kafka Flink Spark Iceberg Delta Hudi S3 GCS API Streaming
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Description

Senior/Staff Engineer, BE BI

Location: Bangkok, Thailand

Department: Information Technology

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:

We’re building and operating Agoda’s high‑performance analytics platform that powers BI and product decisioning at scale. Our team partners closely with Data Engineering, BI, and Product to deliver governed, reliable, and cost‑efficient analytical data services. We work hands-on with distributed systems in production, with strong engineering ownership (SLOs, observability, incident response) and the opportunity to contribute back to open source.

The Opportunity:

This role is for an experienced backend engineer to design, build, and operate our StarRocks-based analytics platform. You’ll drive scalable, low‑latency analytical services with high concurrency and sub‑second query performance, while extending StarRocks capabilities and improving platform reliability and efficiency. The scope is open from Senior through Principal, depending on impact and leadership.

What You’ll Do:

  • Design, implement, and operate StarRocks clusters to support high concurrency and sub‑second analytical queries at scale.
  • Plan and execute StarRocks upgrades and patch rollouts with minimal downtime (canary/blue‑green), including compatibility validation, regression testing, and rollback plans.
  • Develop features, extensions, and performance improvements to increase StarRocks stability and throughput; contribute upstream when broadly useful.
  • Build backend services and tooling around StarRocks for ingestion, connectors, governance, and observability.
  • Optimize query performance using materialized views, partitioning, statistics, and resource groups; make cost/performance trade‑offs.
  • Implement multi‑tenant controls, RBAC, auditing, and data protection to ensure governed access.
  • Automate operations (provisioning, autoscaling, backup/restore, upgrades) using Kubernetes and Infrastructure as Code.
  • Collaborate with Data Engineering, BI, and Product on data modeling and semantic layers.
  • Define and monitor SLOs, build dashboards, participate in on‑call rotations, and lead performance/reliability incident response.

Required Qualifications:

  • 6+ years of backend engineering and/or distributed systems experience (or equivalent impact).
  • Expert proficiency in modern C++ (C++17/20), especially for performance‑critical systems (concurrency, memory management, templates, profiling).
  • Strong foundations in algorithms, concurrency, networking, and Linux systems.
  • Hands-on experience with at least one MPP/OLAP system such as StarRocks, ClickHouse, Presto/Trino, Druid, Pinot, Doris, Impala, etc.
  • Practical expertise in query tuning and columnar analytics concepts (e.g., vectorized execution, statistics, cost-based optimization).
  • Proven experience operating distributed systems in production (monitoring, alerting, incident response, capacity planning).
  • Experience with containers and orchestration (Docker, Kubernetes) and Infrastructure as Code (Terraform/Helm/Ansible).
  • Strong communication skills and ability to partner effectively across platform, BI, and infrastructure teams.

Preferred Qualifications:

  • Direct experience with StarRocks internals (FE/BE), materialized views, tablet management, and Lakehouse/Iceberg integration.
  • Contributions to open-source OLAP/DBMS projects (StarRocks or related ecosystems).
  • Experience building streaming ingestion (Kafka, Flink) and batch ingestion (Spark) into OLAP stores.
  • Knowledge of data governance, security, and compliance for analytics platforms.
  • Familiarity with cloud object storage and Lakehouse table formats (Iceberg, Delta, Hudi) and S3/GCS-compatible storage.
  • Deep performance tuning experience (profilers, SIMD/vectorization, CPU/memory optimization).

#Bangkok #Thailand

 

 

 

Please review our Hiring Process Guidelines before your interview — click here to learn how interviewing at Agoda works.

 

 

Discover more about working at Agoda

 

Equal Opportunity Employer 

At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting diverse talent and cultivating an inclusive environment that encourages collaboration and innovation. Employment at Agoda is based solely on a person’s merit and qualifications. We are committed to providing equal employment opportunity regardless of sex, age, race, color, national origin, religion, marital status, pregnancy, sexual orientation, gender identity, disability, citizenship, veteran or military status, and other legally protected characteristics.

We will keep your application on file so that we can consider you for future vacancies and you can always ask to have your details removed from the file. For more details please read our privacy policy.

Disclaimer

We do not accept any terms or conditions, nor do we recognize any agency’s representation of a candidate, from unsolicited third-party or agency submissions. If we receive unsolicited or speculative CVs, we reserve the right to contact and hire the candidate directly without any obligation to pay a recruitment fee.

Agoda
Agoda

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