Data Engineer - Insurance, Regional, 12 months

Michael Page International (HK) Ltd; Hong Kong Island; On-site; HKD 420,000 - 720,000; Full time

Job summary

Michael Page International (HK) Ltd is seeking a Data Engineer in the insurance sector to design, develop, and maintain data pipelines supporting reporting, analytics, and operations.

You will optimise data models, work with ETL/ELT processes, and develop MI reports and dashboards using Power BI, Tableau or similar tools, while collaborating with business and tech teams on cloud platforms like Databricks, AWS, Azure or GCP.

Qualifications

• Fresh graduates welcome with strong academic performance and relevant internship/project experience.
• 1–2 years of experience in data engineering, data analytics, BI, or related fields preferred.
• Experience in banking, insurance or regulated industries is advantageous but not required.
• Hands-on programming in Python and SQL.
• Experience with data visualization tools (Power BI, Tableau, Qlik Sense, Looker Studio).
• Exposure to cloud platforms such as Databricks, AWS, Azure or GCP.
• Understanding data modelling, ETL/ELT, and data warehousing concepts.
• Knowledge of ML/AI projects is a plus.

Responsibilities

• Design, develop, and maintain data pipelines to support reporting, analytics, and operations.
• Assist in data modelling, schema design, and database development for quality and scalability.
• Perform ETL/ELT from multiple data sources.
• Develop MI reports, dashboards, and visualizations for business stakeholders.
• Support cloud-based data platforms and aid data migration and transformation initiatives.
• Collaborate with business users, analysts, and tech teams to meet data needs.
• Assist in data governance, quality monitoring, and documentation.
• Participate in AI/ML initiatives and PoCs where applicable.

Skills

Python

SQL

Data viz tools

Power BI

Tableau

Qlik Sense

Looker Studio

Databricks

AWS

Azure

GCP

ETL/ELT

Data warehousing

ML/AI awareness

Tools

Power BI

Tableau

Qlik Sense

Looker Studio

Databricks

AWS

Azure

GCP

Job description

As a Data Engineer within the insurance industry, you will play a crucial role in managing and optimising data systems and processes. This position involves leveraging your technical skills to support the organisation's technology initiatives effectively.

Leading insurance group

Description

As a Data Engineer, your main responsibilities will include:

• Design, develop, and maintain data pipelines to support reporting, analytics, and operational requirements.
• Assist in data modelling, schema design, and database development to ensure data quality and scalability.
• Perform data extraction, transformation, and loading (ETL/ELT) activities from multiple data sources.
• Develop management information (MI) reports, dashboards, and visualizations for business stakeholders.
• Support cloud-based data platforms and contribute to data migration and transformation initiatives.
• Collaborate with business users, analysts, and technology teams to understand and deliver data requirements.
• Assist in data governance, data quality monitoring, and documentation activities.
• Participate in AI/ML-related initiatives and proof-of-concepts where applicable.

Profile

A successful Data Engineer should have:

• Fresh graduates with strong academic performance and relevant internship/project experience are welcome.
• 1-2 years of experience in data engineering, data analytics, business intelligence, or related fields is preferred.
• Experience within banking, insurance, financial services, or other regulated industries is advantageous but not mandatory.
• Hands‑on programming experience in Python, SQL, or other relevant programming languages.
• Experience with data visualization and dashboard development using tools such as Power BI, Tableau, Qlik Sense, Looker Studio (Data Studio), or similar platforms.
• Exposure to cloud-based data platforms such as Databricks, AWS, Azure, Google Cloud Platform (GCP), or equivalent technologies.
• Understanding of database concepts, data modelling, ETL/ELT processes, and data warehousing principles.
• Knowledge or experience in Machine Learning (ML) and Artificial Intelligence (AI) projects would be an advantage.