Data Engineer – Cloud Platform (GCP)

About the role

We are looking for a passionate Data Engineer to help build a next‑generation, cloud‑based data platform for a leading telecommunications company in Hong Kong. You will work on large‑scale data pipelines, enable analytics, and support business insights across the organization.

Key responsibilities

• Design, build, and maintain robust data pipelines on Google Cloud Platform (GCP) to ingest and transform high‑volume data.
• Develop and optimise data marts and data warehouse structures for Business Intelligence and analytical workloads.
• Perform feature engineering and data preparation for advanced analytics and machine‑learning models.
• Implement software components following architectural standards, security practices, and coding guidelines.
• Conduct unit testing, support system and user‑acceptance testing, and assist with production roll‑out.
• Document system architecture, operations, and data flows comprehensively.
• Translate business requirements into technical specifications, perform data analysis, gap assessments, and process mapping.
• Partner with stakeholders to define KPIs, reporting needs, and analytical frameworks.
• Support data‑governance initiatives, including metadata management, data‑quality monitoring, and compliance alignment.
• Lead and mentor junior engineers, manage data‑engineering projects, and ensure deliverables meet business and technical standards.

Required profile

• Bachelor’s degree in Computer Science, Information Technology, Data Science or a related field.
• Minimum 5 years of experience in data engineering, data warehousing, or BI development, with proven delivery and stakeholder‑facing experience.
• Strong ability to work cross‑functionally with data scientists, BI analysts, and business units.

Required skills

• Google Cloud Platform (GCP)
• Big Data technologies
• Cloud computing
• Event‑driven architecture
• Live data streaming
• Data warehousing and data marts
• Business Intelligence (BI) support
• Feature engineering for machine‑learning
• Software development standards and security practices
• Unit testing and documentation
• Metadata management and data‑quality monitoring