We are seeking passionate and skilled professionals to join our team in building a next-generation cloud-based data platform for one of the largest telecommunications companies in Hong Kong. This platform is a core initiative in the company’s transformation toward becoming a data-driven enterprise, leveraging cutting-edge technologies such as Big Data, Cloud computing, event-driven architecture, live data streaming, and advanced data governance frameworks.
Candidates will play a key role in data solution development, analytics enablement, and business insights delivery, collaborating closely with Data Scientists, BI Analysts, and cross-functional stakeholders across the organization.
Key Responsibilities
- Design, build, and maintain robust data pipelines ingesting and transforming large volumes of data into Google Cloud Platform (GCP).
- Develop and optimize data marts and data warehouse structures to support Business Intelligence (BI) and analytical workloads.
- Conduct 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 implementation.
- Develop and maintain comprehensive documentation for systems, operations, and data flows.
- Provide ongoing user and application support, ensuring reliability and performance of production data environments.
Business Analysis & Data Governance
- Translate business requirements into clear technical specifications, working closely with business units and IT teams.
- Perform data analysis, gap assessments, and process mapping to identify improvement opportunities.
- Partner with stakeholders to define KPIs, reporting requirements, and analytical frameworks supporting business decision-making.
- Support and execute data governance initiatives, including metadata management, data quality monitoring, and compliance alignment.
- Facilitate effective communication between business and technical teams, ensuring alignment on objectives, priorities, and deliverables.
- Lead and manage data engineering projects across diverse industry sectors (Public, Enterprise, BFSI).
- Drive project planning and execution for data pipeline and data management initiatives.
- Mentor and guide junior data engineers and analysts to achieve project and professional goals.
- Collaborate with stakeholders to ensure project deliverables meet business, technical, and compliance requirements.
Qualifications and Experience
- Bachelor’s degree in Computer Science, Information Technology, Data Science, or related disciplines.
- Data Engineer: Minimum 3 years of experience in data engineering, data warehouse, or BI development.
- Senior Data Engineer / Business Analyst: Minimum 5 years of experience with proven technical delivery and stakeholder-facing experience.
- Hands-on experience in data warehouse/lake design, ETL development, and cloud data platforms (preferably GCP, AWS, or Azure).
- Proficiency in SQL, Python, and other relevant programming or scripting languages.
- Good understanding of RDBMS, NoSQL, and data visualization tools (e.g., Power BI, Tableau, or Looker).
- Familiarity with Big Data frameworks, Cloud-native technologies, and Data-as-a-Service (DaaS) platforms is a strong advantage.
- Demonstrated analytical thinking with the ability to interpret complex business problems and convert them into actionable data solutions.
- Excellent communication, documentation, and interpersonal skills; fluent in English and Chinese (spoken and written).
- Project management experience or certifications (e.g., PRINCE2, PMP, or Scrum Master) are advantageous but not essential.
Ideal Candidate Attributes
- Strong analytical and problem-solving mindset with attention to data quality and integrity.
- Collaborative team player with a proactive “can-do” attitude.
- Fast learner who embraces new technologies and continuous improvement.
- Comfortable working in dynamic, fast-paced environments under agile methodologies.
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