General Information

Req #

100017481

Career area:

Data Management and Analytics

Country/Region:

Hong Kong

City:

Hong Kong

Date:

Monday, August 17, 2026

Additional Locations:

* Hong Kong

Why Work at Lenovo

We are Lenovo. We do what we say. We own what we do. We WOW our customers.

Lenovo is a US$83 billion revenue global technology powerhouse, ranked #153 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

To find out more visit www.lenovo.com and read about the latest news via our StoryHub.

Description and Requirements

About the Practice

Lenovo PCCW Solutions (LPS) AI Data Intelligence Practice helps enterprises become truly AI-ready through data strategy, AI readiness, data governance, modern data platforms, analytics, generative AI and managed services. The practice delivers end-to-end services across Strategy, Build and Adopt, helping clients turn enterprise data into trusted business assets and AI-ready foundations.

We work with leading platforms and ecosystems including Microsoft Fabric, Databricks, Azure, AWS, Alibaba Cloud, Lenovo Hybrid AI, modern data governance platforms, data product frameworks, and enterprise AI technologies.

Our clients span financial services, government and public sector, manufacturing, education, retail, hospitality, property, logistics and enterprise markets across Hong Kong and Greater China.

Role Overview

As a Senior Data Engineer, you will design, build and optimize secure, scalable and reliable data pipelines that power analytics, AI and enterprise reporting workloads. You will work across batch, streaming, API and cloud-native integration patterns.

Key Responsibilities

Data Engineering

· Design and develop ETL/ELT pipelines for enterprise data platforms.

· Build batch, CDC, streaming and API-based data integration solutions.

· Implement data validation, data quality, reconciliation and error handling frameworks.

· Create reusable data engineering components, templates and accelerators.

· Support data modelling, transformation and data serving requirements.

Platform Engineering

· Develop cloud-native data solutions on modern data platforms.

· Support data platform migration, modernization and performance optimization initiatives.

· Implement monitoring, logging, alerting and operational observability for data pipelines.

· Work with architects to translate solution designs into robust engineering deliverables.

Engineering Excellence

· Apply DataOps, DevOps, CI/CD and version control best practices.

· Optimize data workloads for scalability, reliability and cost efficiency.

· Document technical designs, deployment procedures and operational runbooks.

Required Qualifications

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

· 3-8 years of experience in data engineering, ETL development, data platform implementation or cloud data projects.

· Strong SQL and Python skills.

· Experience with Spark, Databricks, Microsoft Fabric, Azure Data Factory, Kafka, Airflow or equivalent tools.

· Good understanding of data integration, data quality and pipeline orchestration concepts.

Preferred Qualifications

· Experience with Git, CI/CD, Terraform, Docker or cloud-native deployment practices.

· Exposure to streaming, CDC, DataOps, cloud migration or lakehouse implementations.

· Experience supporting enterprise analytics, AI, reporting or managed service environments.

What Success Looks Like

· Pipelines are reliable, secure, observable and easy to operate.

· Data is delivered where it is needed with quality, timeliness and scalability.

· Engineering patterns become reusable assets across multiple client engagements.

Working Style

· Collaborate with business stakeholders, architects, engineers, consultants and service delivery teams.

· Operate in a consulting-led delivery environment with strong ownership of outcomes.

· Contribute to reusable methodology, accelerators and solution assets for the practice.

· Support enterprise customers in Hong Kong with occasional regional collaboration as required.

Why Join Us

· Join a growing AI Data Intelligence Practice focused on strategy, platform, analytics, AI and managed services.

· Work on enterprise-scale transformation programs with modern data and AI platforms.

· Develop cross-disciplinary capability across consulting, architecture, engineering, data products and adoption.

Help clients build AI-ready data foundations that support BI, ML, Gen BI and AI agents.