Senior Data Engineer

Nexus Corporation; Hong Kong; On-site; HKD 800,000 - 1,100,000; Full time

Job summary

Nexus Corporation in Hong Kong is seeking a Senior Data Engineer to deliver high-quality data solutions across business demands, building and maintaining pipelines, datasets, and data services that are reliable, secure, and easy to consume. This is a hands-on engineering role.

This role requires strong Python/Java, Spark, Airflow, and cloud experience to design, deploy, and run data products while ensuring governance, data quality, and performance.

Qualifications

• Minimum 8 years of hands-on experience delivering data engineering solutions.
• Strong programming skills in Python and Java, advanced SQL, and solid data modelling skills.
• Experience with distributed processing (e.g. Spark) and orchestration (e.g. Airflow).
• Experience with streaming platforms (e.g. Kafka/Pub/Sub) is beneficial; ability to learn quickly if not.
• Strong engineering discipline: Git, code reviews, automated testing, CI/CD, and observability.
• Experience with cloud data platforms: Azure/AWS/GCP and lake/lake-house/warehouse patterns.
• Proven ability to manage multiple data requests and priorities effectively and deliver at pace.

Responsibilities

• Build and maintain batch and/or streaming data pipelines end-to-end: ingest, transform, validate, and 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 through query tuning, partitioning, and efficient computer usage.
• Maintain clear documentation, data definitions, and runbook 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, and audibility.

Skills

Python

Java

SQL

Data Modelling

Airflow

Kafka

Git

CI/CD

Observability

Tools

Spark

Azure

AWS

GCP

Docker

Kubernetes

Job description

Role purpose: We're hiring a Senior Data Engineer to deliver high-quality data solutions across a range of business demands, building and maintaining pipelines, datasets, and data services that are reliable, secure, and easy to consume. This is a hands-on engineering role.

Key responsibilities

• Build and maintain batch and/or streaming data pipelines end-to-end: ingest, transform, validate, and 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 through query tuning, partitioning, and efficient computer usage.
• Maintain clear documentation, data definitions, and runbook 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, and audibility.

Requirements

Required skills and experience:

• Minimum 8 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/lake-house/warehouse patterns.
• Proven ability to manage multiple data requests and priorities effectively and deliver at pace.