Lead Data Engineer at AIRR LABS
Singapore 238997, , Singapore -
Full Time


Start Date

Immediate

Expiry Date

15 Nov, 25

Salary

0.0

Posted On

16 Aug, 25

Experience

3 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Sql, Mapreduce, Kubernetes, Python, Operations, Scripting Languages, Data Architecture, Computer Engineering, Computer Science, Cloud Services, Data Engineering

Industry

Information Technology/IT

Description

AIRR LABS is building lasting connections between brands and consumers in Southeast Asia. We provide a full suite of solutions that enable brands to bring their unique stories to life across the online customer journey. We are looking for curious and highly motivated talent who can seize exciting market opportunities and turn them into impactful brand strategies and execution.

REQUIREMENTS

  • Bachelor’s or higher degrees in Computer Science, Computer Engineering or other relevant degrees.
  • 5+ years of experience in data engineering, with at least 3 years in a senior or lead role.
  • Strong technical expertise in designing scalable and reliable data architecture. Extensive hands-on experience with Python, SQL, and other scripting languages is a must.
  • Proficient in AWS cloud services e.g. EC2, S3 and Lambda
  • Proven experience in setting up and maintaining workflow orchestration tools (e.g., Apache Dolphin).
  • Familiarity with distributed computing frameworks (e.g., Spark, MapReduce). Understanding of Kubernetes cluster architecture and operations is a must.
  • Self-motivated, proactive, and eager to work in a fast-paced environment. Prior experience in the e-commerce industry is a plus.
    Job Types: Full-time, Permanent

Experience:

  • Senior or Lead Data Engineer: 3 years (Preferred)

Work Location: In perso

Responsibilities
  • Lead the design, development, and implementation of scalable and efficient data pipelines, databases, and infrastructure on cloud solutions.
  • Implement ETL processes to integrate data from various sources, ensuring data quality, consistency, and integrity.
  • Optimize data pipelines, queries, and infrastructure for performance, scalability, and cost-efficiency.
  • Collaborate with cross-functional teams to gather requirements, assess technological options, and propose architectural solutions.
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