Senior Data Engineer (Databricks)

EPAM Systems; Hong Kong; On-site; HKD 600,000 - 1,000,000; Full time

Job summary

EPAM Systems in Hong Kong seeks a Senior Data Engineer to design and build cloud-based data products for insurance analytics. You will create reliable pipelines on Microsoft Azure using Azure Databricks and Python, while improving data quality and governance to deliver trusted insights.

Responsibilities include ETL/ELT development in Azure Databricks, batch and streaming solutions with ADF, Kafka and Event Hubs, and collaboration with stakeholders to define requirements.

Qualifications

• Experience designing data pipelines on Azure Databricks and Spark.
• Proficiency with Azure data services (ADF, Data Lake Gen2) and Spark.
• Clear communication in Cantonese, Chinese and English.

Responsibilities

• Design, build and maintain scalable ETL/ELT pipelines in Azure Databricks.
• Develop batch and streaming data solutions using Azure Data Factory, Data Lake Gen2, Event Hubs and Kafka.
• Collaborate with data architects and stakeholders to define requirements and deliver data assets.
• Implement data quality checks, governance, metadata and lineage tooling (Informatica).
• Apply security controls including RBAC, encryption and secrets management (Azure Key Vault).
• Improve deployment reliability with GitHub Actions automation.
• Troubleshoot pipeline issues and perform root cause analysis.
• Maintain documentation for transformations, testing and runbooks.

Skills

Azure Databricks

Apache Spark

Python

Data governance

RBAC security

GitHub Actions

Stakeholder communication

Streaming data

Tools

Azure Data Factory

Azure Data Lake Storage Gen2

Azure Event Hubs

Apache Kafka

Informatica

Azure Key Vault

Job description

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