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Cube

Data Engineer (Digital Shelf Team)

Cube Bangkok, Thailand

Technology, Information and Internet · 11-50 employees

16 h ago
Remote data-engineer Mid (2-5 yrs) Full-time Thailand
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About the role

The Data Engineer will manage the full data lifecycle, including ingestion, transformation, and delivery for the Digital Shelf Team. They are responsible for maintaining data pipelines, ensuring data quality, and automating operational workflows to support client deliverables.

What they look for

Python SQL Data Engineering ETL Data Pipelines Data Quality Management Data Analysis AWS S3 Airflow Data Processing Orchestration Tools Analytical Thinking Data Validation Anomaly Detection Documentation

Requirements

Candidates must have 2-4 years of hands-on experience in data engineering and a bachelor's degree in a quantitative or technical field. Proficiency in Python and SQL, along with experience in ETL processes and cloud data platforms, is required.

Full description

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.
  • 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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