Senior/Staff Data Platform Engineer - (Bangkok based, relocation provided)
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Key skills for this role
About the Role
Agoda is looking for a Senior/Staff Data Platform Engineer to design, build, and operate a cloud-native data platform on Snowflake and public cloud infrastructure. The role involves provisioning cloud environments, architecting Snowflake, enforcing data access policies, and building dbt transformation models.
Key Skills for This Role
Responsibilities
- Provision and manage cloud environments including storage, networking, and IAM across public cloud platforms (AWS / GCP / Azure)
- Architect and administer Snowflake as the organization's unified Data Lake and Data Warehouse platform
- Define and enforce Data Access Policies with encryption, IAM, and Snowflake network policies
- Build and maintain dbt transformation models with Git based version control across staging and production data layers
- Define platform standards, and best practices for data ingestion, transformation, and consumption layers
Requirements
- Hands on experience with at least one major public cloud platform (AWS, GCP, or Azure), covering storage, networking, and IAM
- Strong Snowflake administration skills including RBAC design, Data Sharing, external tables, and Snowpark
- Proficiency in dbt (Core or Cloud) for model development, testing, and documentation
- Experience with Infrastructure as Code tooling such as Terraform for cloud resource provisioning
- Experience with pipeline orchestration using Snowflake Scheduling, Apache Airflow, or equivalent tools
Full Job Posting
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.
Position Overview
- The Data Platform Engineer is responsible for designing, building, and operating a cloud native data platform built on Snowflake and public cloud infrastructure. This role owns the full lifecycle of the data platform — from cloud environment provisioning and data lake architecture to transformation
Key Responsibilities
- Provision and manage cloud environments including storage, networking, and IAM across public cloud platforms (AWS / GCP / Azure).
- Architect and administer Snowflake as the organization's unified Data Lake and Data Warehouse platform.
- Define and enforce Data Access Policies with encryption, IAM, and Snowflake network policies.
- Build and maintain dbt transformation models with Git based version control across staging and production data layers.
- Define platform standards, and best practices for data ingestion, transformation, and consumption layers.
Required Skills & Experience
- Hands on experience with at least one major public cloud platform (AWS, GCP, or Azure), covering storage, networking, and IAM.
- Strong Snowflake administration skills including RBAC design, Data Sharing, external tables, and Snowpark.
- Proficiency in dbt (Core or Cloud) for model development, testing, and documentation.
- Experience with Infrastructure as Code tooling such as Terraform for cloud resource provisioning.
- Experience with pipeline orchestration using Snowflake Scheduling, Apache Airflow, or equivalent tools.
Scope of Ownership
- Responsible for the end to end design, provisioning, and operation of the cloud data platform — spanning raw ingestion zones through to curated, analytics ready data layers — ensuring reliability, scalability, and cost efficiency across all environments.
- Maintains overall responsibility for the unified data warehouse and lake platform architecture, including performance tuning, resource management, cost governance, and the evolution of the platform as organizational data needs grow.
- Owns the transformation layer from design through to production, ensuring data models, testing frameworks, and documentation are accurate, consistent, and fit for consumption across all downstream use cases.
- Drives the definition and adoption of platform wide standards covering data ingestion, transformation, and consumption patterns, and evaluates tooling and architectural decisions as the platform matures.
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