- Hong Kong Hong Kong
Capgemini Hiring! Full Time Data Engineer in Hong Kong - Ricebowl
Working Location
Job Description
Responsibilities
Key responsibilities
Build and maintain batch and/or streaming data pipelines end-to-end (ingest, transform, validate, publish)
Develop reusable data transformation patterns and curated datasets for analytics and operational use cases
Work from requirements through to production delivery: design, build, test, deploy, and support
Implement data quality checks, reconciliations, and monitoring/alerting to keep data trustworthy
Optimize performance and cost (query tuning, partitioning, efficient compute usage)
Maintain clear documentation, data definitions, and runbooks for supported pipelines
Troubleshoot production issues, perform root cause analysis, and implement permanent fixes
Collaborate with upstream/downstream teams to resolve data issues and improve interfaces/contracts
Follow security and governance expectations for sensitive data (access controls, auditability)
Required skills and experience
Minimum 5 years of strong hands-on experience delivering data engineering solutions
Strong programming skills in Python and/or Java experience, with advanced SQL and solid data modelling skills
Experience with distributed processing (e.g. Spark) and orchestration (e.g. Airflow or equivalent)
Experience with streaming/event platforms (e.g. Kafka/PubSub) is beneficial; ability to learn quickly if not
Strong engineering discipline: Git, code reviews, automated testing, CI/CD, and observability
Experience working with cloud data platforms (Azure/AWS/GCP) and lake/lakehouse/warehouse patterns
Proven ability to manage multiple data requests, priorities effectively, and deliver at pace
Full-time
Build and maintain batch and/or streaming data pipelines end-to-end (ingest, transform, validate, publish)
Develop reusable data transformation patterns and curated datasets for analytics and operational use cases
Work from requirements through to production delivery: design, build, test, deploy, and support
Implement data quality checks, reconciliations, and monitoring/alerting to keep data trustworthy
Optimize performance and cost (query tuning, partitioning, efficient compute usage)
Maintain clear documentation, data definitions, and runbooks for supported pipelines
Troubleshoot production issues, perform root cause analysis, and implement permanent fixes
Collaborate with upstream/downstream teams to resolve data issues and improve interfaces/contracts
Follow security and governance expectations for sensitive data (access controls, auditability)
Required skills and experience
Minimum 5 years of strong hands-on experience delivering data engineering solutions
Strong programming skills in Python and/or Java experience, with advanced SQL and solid data modelling skills
Experience with distributed processing (e.g. Spark) and orchestration (e.g. Airflow or equivalent)
Experience with streaming/event platforms (e.g. Kafka/PubSub) is beneficial; ability to learn quickly if not
Strong engineering discipline: Git, code reviews, automated testing, CI/CD, and observability
Experience working with cloud data platforms (Azure/AWS/GCP) and lake/lakehouse/warehouse patterns
Proven ability to manage multiple data requests, priorities effectively, and deliver at pace
Full-time
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