We are seeking a Senior Data Engineer to build cloud-based data products that power insurance analytics and reporting. You will design reliable pipelines on Microsoft Azure using Azure Databricks and Python, improve data quality and governance and partner with data and business teams to turn complex datasets into trusted insights.
Responsibilities
Design, build and maintain scalable ETL and ELT pipelines in Azure Databricks
Develop batch and streaming data solutions using Azure Data Factory, Azure Data Lake Storage Gen2, Azure Event Hubs and Kafka
Partner with data architects, analysts and business stakeholders to define requirements and deliver fit-for-purpose data assets
Implement data quality checks, validation, cleansing and governance including Informatica lineage, metadata and cataloging
Apply security and compliance controls including role-based access control (RBAC), managed identities and encryption aligned to Hong Kong Personal Data (Privacy) Ordinance (PDPO) and General Data Protection Regulation (GDPR)
Improve deployment reliability through GitHub Actions automation and solid operational practices
Troubleshoot pipeline issues, perform root cause analysis and drive performance tuning
Maintain clear documentation for transformations, testing and runbooks
Requirements
Hands‑on experience building data pipelines with Azure Databricks and Apache Spark (PySpark or Scala)
Strong background with Microsoft Azure data services including Azure Data Factory and Azure Data Lake Storage Gen2
Proficiency in Python for data transformation and automation
Experience working with streaming platforms such as Apache Kafka or Azure Event Hubs
Knowledge of data governance and data quality practices including metadata and lineage tools such as Informatica
Understanding of security and compliance controls including RBAC, encryption and secrets management such as Azure Key Vault
Practical experience with continuous integration and continuous delivery (CI/CD) automation such as GitHub Actions
Clear communication in Cantonese, Chinese and English
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Responsibilities
Design, build and maintain scalable ETL and ELT pipelines in Azure Databricks
Develop batch and streaming data solutions using Azure Data Factory, Azure Data Lake Storage Gen2, Azure Event Hubs and Kafka
Partner with data architects, analysts and business stakeholders to define requirements and deliver fit-for-purpose data assets
Implement data quality checks, validation, cleansing and governance including Informatica lineage, metadata and cataloging
Apply security and compliance controls including role-based access control (RBAC), managed identities and encryption aligned to Hong Kong Personal Data (Privacy) Ordinance (PDPO) and General Data Protection Regulation (GDPR)
Improve deployment reliability through GitHub Actions automation and solid operational practices
Troubleshoot pipeline issues, perform root cause analysis and drive performance tuning
Maintain clear documentation for transformations, testing and runbooks
Requirements
Hands‑on experience building data pipelines with Azure Databricks and Apache Spark (PySpark or Scala)
Strong background with Microsoft Azure data services including Azure Data Factory and Azure Data Lake Storage Gen2
Proficiency in Python for data transformation and automation
Experience working with streaming platforms such as Apache Kafka or Azure Event Hubs
Knowledge of data governance and data quality practices including metadata and lineage tools such as Informatica
Understanding of security and compliance controls including RBAC, encryption and secrets management such as Azure Key Vault
Practical experience with continuous integration and continuous delivery (CI/CD) automation such as GitHub Actions
Clear communication in Cantonese, Chinese and English
#J-18808-Ljbffr
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