Data Engineer

IT Channel (Asia) Limited; Hong Kong; On-site; HKD 480,000 - 720,000; Full time

Job summary

IT Channel (Asia) Limited in Hong Kong seeks a data engineer to design, build, and maintain scalable ETL/ELT pipelines on Azure Databricks for diverse insurance data, collaborating with data architects and stakeholders to deliver robust assets. You will ensure data quality, governance, and security while integrating batch and streaming sources such as Kafka.

Proficiency in PySpark/Scala, Python, and Informatica, plus CI/CD with GitHub Actions, is essential for timely analytics and regulatory

Qualifications

• Bachelor's degree in Information Technology, Computer Science, Data Engineering, or related discipline.
• 3+ years of experience as a data engineer, building and maintaining ETL/ELT processes on Azure Databricks using PySpark or Scala.
• Strong experience orchestrating data ingestion, transformation, and loading with Azure Data Factory and Data Lake Gen2.
• Advanced proficiency in Python and Spark for data engineering and feature engineering in Databricks.
• Experience integrating batch and streaming data sources via Kafka or Azure Event Hubs for real-time insurance use cases.
• Hands-on use of Informatica for data quality, lineage, and governance to support regulatory standards.
• Familiarity with automation and CI/CD of Databricks workflows using GitHub Actions.
• Understanding of data security, RBAC, Key Vault, encryption, and compliance in the insurance sector.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines in Azure Databricks for insurance data.
• Collaborate with data architects, modellers, analysts, and stakeholders to gather requirements.
• Develop, test, and optimize data transformation routines, including batch and streaming solutions.
• Implement data quality, validation, and cleansing procedures to ensure reliability.
• Integrate Informatica tools for governance, lineage, and metadata capture as required.
• Ensure robust data security and compliance with HK PDPO and related regulations.
• Automate deployment pipelines using GitHub Actions for auditable data workflows.
• Produce comprehensive technical documentation for pipelines and processes.

Skills

PySpark

Scala

Azure Databricks

Azure Data Factory

Data Lake Gen2

Kafka

Informatica

Python

Spark

RBAC & security

Data governance

Education

Bachelor's degree in IT/CS/Data Engineering

Tools

Informatica

GitHub Actions

Azure Data Factory

Azure Databricks

Job description

Design, build, and maintain scalable and efficient ETL/ELT pipelines in Azure Databricks to process structured, semi-structured, and unstructured insurance data from multiple internal and external sources.

Collaborate with data architects, modellers, analysts, and business stakeholders to gather data requirements and deliver fit-for-purpose data assets that support analytics, regulatory, and operational needs.

Develop, test, and optimize data transformation routines, batch and streaming solutions (leveraging tools such as Azure Data Factory, Data Lake Storage Gen2, Azure Event Hubs, and Kafka) to ensure timely and accurate data delivery.

Implement rigorous data quality, validation, and cleansing procedures—with a focus on enhancing reliability for high-stakes insurance use cases, reporting, and regulatory outputs.

Integrate Informatica tools to facilitate data governance, including the capture of data lineage, metadata, and data cataloguing as required by regulatory and business frameworks.

Ensure robust data security by following best practices for RBAC, managed identities, encryption, and compliance with Hong Kong's PDPO, GDPR, and other relevant regulatory requirements.

Automate and maintain deployment pipelines using GitHub Actions to ensure efficient, repeatable, and auditable data workflows and code releases.

Conduct root cause analysis, troubleshoot pipeline failures, and proactively identify and resolve data quality or performance issues.

Produce and maintain comprehensive technical documentation for pipelines, transformation rules, and operational procedures to ensure transparency, reuse, and compliance.

Apply subject matter expertise in Hong Kong Life and General Insurance to ensure that development captures local business needs and industry-specific standards.

Requirement / Experience

Bachelor's degree in Information Technology, Computer Science, Data Engineering, or a related discipline.

3+ years of experience as a data engineer, building and maintaining ETL/ELT processes and data pipelines on Azure Databricks (using PySpark or Scala), with a focus on structured, semi-structured, and unstructured insurance data.

Strong experience orchestrating data ingestion, transformation, and loading workflows using Azure Data Factory and Azure Data Lake Storage Gen2.

Advanced proficiency in Python and Spark for data engineering, data cleaning, transformation, and feature engineering in Databricks for analytics and machine learning.

Experience integrating batch and streaming data sources via Kafka or Azure Event Hubs for real-time or near-real-time insurance applications.

Hands-on use of Informatica for data quality, lineage, and governance to support business and regulatory standards in insurance.

Familiarity with automation and CI/CD of Databricks workflows using GitHub Actions.

Understanding of data security, RBAC, Key Vault, encryption, and best practices for compliance in the insurance sector.

Experience optimizing data pipelines to support ML workflows and BI/reporting tools.

With its headquarters in Hong Kong, IT Channel (Asia) Limited is one of Asia Pacific's leading Business Solution Integrators. We bring the world's latest technological products and solutions to our customers, helping them maximize the return on their technology investment by skillfully integrating innovative business solutions, advanced technology and complex system infrastructures.