The Role
The Data Engineer will build and optimize data infrastructure, focusing on ETL jobs, data orchestration, and working with Data Scientists to ensure data accessibility and integrity.
Summary Generated by Built In
We are seeking a highly motivated and experienced Data Engineer to join our technology team in Hong Kong. In this critical role, you will be responsible for building, optimizing, and maintaining our enterprise-level data infrastructure, spanning both streaming and batch processing. You will leverage Python to solve complex engineering challenges, working closely with Data Scientists and Product Analysts to ensure data accessibility and reliability for our product applications and business intelligence needs. The ideal candidate possesses strong proficiency in Python, expert knowledge of SQL and data orchestration tools, and a passion for operational excellence using DataOps and MLOps principles.
What You'll Deliver
What You'll Deliver
- Build/Maintain/Optimize robust ETL jobs and high-throughput pipelines for both streaming and batch data processing.
- Help to conquer complex engineering problems encountered within the data science and analytics domain.
- Develop and productionize data-driven product applications and API services in close collaboration with data scientists and analysts.
- Design and manage data schemas and structures across various database systems to ensure performance and integrity.
- Utilize and maintain modern orchestration tools (like Airflow) to manage complex data workflows.
- Implement containerization (Kubernetes) for deploying and scaling data services reliably.
- Promote and implement DataOps and foundational MLOps practices for automated deployment and monitoring of data services.
- Ensure data quality, governance, and security across all managed pipelines and platforms.
- Participate in code reviews and advocate for engineering best practices.
- Troubleshoot and resolve performance issues within the data platform and pipelines.
Who You Are
- Proven professional experience as a Data Engineer or a similar role focused on data infrastructure.
- Expert-level proficiency in SQL and deep experience working with various databases (relational, NoSQL).
- Strong programming background and proficiency exclusively in Python.
- Hands-on experience with workflow orchestration tools, specifically Apache Airflow.
- Familiarity with containerization and orchestration technologies like Kubernetes.
- Experience in designing, implementing, and optimizing large-scale ETL pipelines (batch and streaming).
- Excellent communication skills in English and Cantonese (written and verbal), with the ability to collaborate effectively in a team environment in Hong Kong.
- Strong problem-solving, debugging, and analytical skills.
- Ability to write clean, maintainable, and well-documented code.
What We Offer
- A diverse, multi-cultural team that values collaboration and innovation.
- Opportunities for continuous learning and professional development in modern data engineering tools.
- The chance to build the data foundation that powers impactful business decisions.
- Contribute to the growth of a leading technology-driven logistics platform in Asia.
- Work on an exciting and high-impact product used by thousands of users.
- Opportunity to work with cutting-edge technologies and shape data infrastructure.
- Dynamic and international work environment.
Top Skills
Airflow
Dataops
Kubernetes
Mlops
Python
SQL
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The Company
What We Do
GoGoX is a leading on-demand logistics and transportation platform in Asia. We operate in over 340 cities across six countries and regions, including Mainland China, Hong Kong, India, Singapore, South Korea and Vietnam. Our mission is to use technology to power the logistics industry’s transformation by providing simple and efficient customer-focused solutions. Services include platform services (real-time delivery for individuals), enterprise services (custom-made solutions for SMEs & large corporations), as well as value-added services (fuel cards and EV rentals). For any enquiries, please send us an email at [email protected] (Hong Kong) [email protected] (Singapore) [email protected] (South Korea) [email protected] (India) [email protected] (Vietnam)
