Senior Data Engineer/Data Platform Engineer

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About AIRR Labs:
AIRR Labs, headquartered in Singapore, is a leading e-commerce enabler in Southeast Asia, specializing in FMCG, Beauty, and Lifestyle brands across platforms such as TikTok Shop, Shopee, and Lazada. Since 2019, AIRR Labs has grown to more than 350 experts across Thailand, the Philippines, Vietnam, Malaysia, and Singapore, with a technology hub in Vietnam.
We partner with global brands including Unilever, Colgate-Palmolive, Estée Lauder, and Adidas to deliver end-to-end digital commerce solutions spanning content strategy, performance marketing, livestreaming, store operations, and fulfillment.
About the Role:
We are looking for a strong Senior Data Engineer to help build and shape the foundation of AIRR Labs' data platform.
This role is ideal for someone who enjoys building systems from 0 to 1, solving ambiguous engineering problems, and designing scalable data architecture - not just maintaining existing pipelines.
As one of the first dedicated hires in this space, you will work closely with software engineers and product teams to define data standards, optimize platform reliability, and build a scalable data foundation that supports analytics and future AI-driven initiatives.
Job Scope:
40% Data Architecture - Partner with product and software engineering teams to design scalable data models, define architecture standards, and ensure clean data flows from source to consumption.
50% Data Engineering - Build and optimize ETL/ELT pipelines, write performant SQL and transformation logic, and maintain data quality across TB-scale datasets.
10% Infrastructure Reliability - Monitor platform uptime, troubleshoot incidents, and keep our cloud data infrastructure healthy.
What You'll Do:
Design and build scalable ETL/ELT pipelines and orchestration workflows.
Develop and optimize data warehouse architecture, data models, and SQL performance across large-scale datasets.
Build reliable batch-processing systems and improve platform observability, monitoring, and fault tolerance.
Define data standards, naming conventions, and best practices for the data platform.
Collaborate with software engineers and stakeholders to solve complex data and system design challenges.
Contribute to future AI and automation initiatives such as intelligent inventory management and self-healing data pipelines.