Data Engineer (Digital Shelf Team)
At a glance
Mid-level Data Engineering role at Cube. Bangkok · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Keep existing pipelines and reporting routines running to support daily operations
- Check data quality through validations and anomaly monitoring, then address reliability issues
- Build and maintain scheduled ETL workflows for analytics and client databases
- Use Python and SQL to clean, reshape and manage information from multiple sources
- Document and standardize data processes while coordinating with teams on new needs
What you bring
- You have two to four years in data engineering, data processing or a related technical role
- A degree or equivalent experience in a quantitative or technical field is required
- You can use Python and SQL proficiently for data processing and analysis
- You understand ETL pipelines and data quality, with familiarity in cloud storage or modern data platforms
Who this fits
This role suits a hands-on data engineer who can own recurring operational workflows and work with technical and non-technical teams. It is an onsite, full-time role in Bangkok.
From the employer
We are seeking an experienced Data Engineer to join our team and support both data analysis and data operations for the Digital shelf Team!
In this role, you will work across the full data lifecycle - from raw data ingestion and validation to transformation, reporting, and downstream delivery. Beyond generating insights, this position plays an important role in maintaining data reliability, improving data quality, and automating operational workflows that support daily client deliverables.
Responsibilities
- Support and maintain existing data pipelines and reporting workflows used for daily operations.
- Implement and monitor data quality checks, validations, and anomaly detection.
- Contribute to building and maintaining scheduled ETL / data processing pipelines to support analytics and client databases (e.g. via orchestration tools, Airflow or similar).
- Use Python and SQL to clean, transform, and manage data from multiple sources.
- Collaborate with cross-functional teams to understand data requirements and support new analytical or operational initiatives.
- Contribute to documentation, standardization, and continuous improvement of data processes and workflows.
Requirements
- 2-4 years of hands-on experience in data engineering, data processing, or a related technical role.
- Bachelor’s degree or equivalent experience in a quantitative or technical field (Statistics, Mathematics, Computer Science, Engineering, or similar).
- Strong proficiency in Python and SQL for data processing and analysis.
- Solid understanding of data pipelines, ETL concepts, and data quality management.
- Familiarity with cloud data storage or modern data platforms (e.g., AWS S3 or similar).
- Strong analytical thinking with high attention to data accuracy, consistency, and structural integrity.
- Ability to work cross-functionally and adapt to different task types and projects.
- Proactive, reliable, and able to take full ownership of recurring data processes and operations.
- Demonstrated ability to work cross-functionally with non-technical stakeholders and cross-domain teams.
- Flexibility to adapt quickly to different task types, evolving requirements, and varied projects.
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