A career at Swire Properties is more than just work, it’s a calling.
We’re searching for exceptional individuals who want to live and breathe “Creative Transformation” – our distinct mindset and long‑term approach that inspires everything we do and how we do it. It inspires us to constantly explore new perspectives and seek out original thinking that goes beyond the conventional. Our mission, ultimately, is to constantly add value to communities and create truly amazing and sustainable places where people can thrive.
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Join us today and work on career‑defining projects that are changing the industry in Hong Kong, the Chinese Mainland, Miami and Southeast Asia.
We are looking for a Data Engineering Lead to join our Data Analytics & Insights team. Reporting to the Head of Data Analytics & Insights, this role is the technical owner of Swire Properties’ Centralised Analytic Platform, responsible for building and operating the enterprise data foundation that powers analytics, customer intelligence, and AI use cases across the Group.
Working closely with the Head of Data Analytics & Insights, this role translates the enterprise data & AI strategy into scalable engineering solutions. It ensures our platform is secure, compliant, scalable, cost‑efficient and AI‑ready, while delivering trusted data products that drive business value.
This role leads a hybrid team of internal data engineers and external partners who works collaboratively with Data Science and Analytics, Data Application & Operations, Data Architecture, Data Governance within the same department as well as the corresponding counterparts in Digital & IT.
RESPONSIBILITIES
Build Trusted, Compliant & Privacy‑Centric Data Foundation
- Implement engineering controls aligned with corporate information security and privacy protection policies and guidelines, as well as data‑quality standards.
- Operationalise privacy and security controls as well as Partner with Digital & IT, Architecture and Cybersecurity to meet regulatory and internal assurance requirements, strengthen security posture and access governance.
- Build and maintain unified, trusted data foundations by integrating core business, CRM, leasing and third‑party data sources.
- Engineer reusable customer data products and optimise privacy‑compliant solutions to enable marketing personalisation, de‑duplication and consent management.
Power Advanced Analytics, ML & GenAI Use Cases (Enablement)
- Build robust analytics datasets and pipelines to support descriptive, diagnostic, predictive and prescriptive use cases.
- Establish scalable DataOps/MLOps capabilities and ensure platform readiness for emerging technologies, including GenAI‑driven productivity and AI‑enabled products.
Drive Engineering Excellence & Modern Delivery Practices
- Define and champion engineering best practices: coding standards, CI/CD pipelines, orchestration, observability, automated testing and documentation.
- Promote Agile and DataOps delivery practices to support high‑velocity analytics development as well as review and sign off technical deliverables.
Ensure Data Platform Reliability, Quality & Operational Performance
- Own platform reliability for the Centralised Analytics Platform, including SLAs, observability, monitoring, pipeline stability and incident response.
- Implement robust data quality & governance practices, with automated validation and comprehensive technical documentation across pipelines, transformations and data flows.
Stakeholder, Vendor & People Leadership
- Lead a hybrid internal–external engineering team, providing technical guidance to senior leadership and ensuring cost‑effective, high‑quality delivery.
- Partner with data, digital, IT and business stakeholders to translate needs into scalable solutions while coaching and performance‑managing the team.
REQUIREMENTS
- Degree in Computer Science, Data Engineering, Information Systems, or a related field.
- 10+ years of data engineering experience, with 3–5 years in a leadership role.
- Hands‑on expertise with cloud data platforms (Azure preferred) and lake / lakehouse / warehouse architecture.
- Strong SQL + Python/PySpark; experience with distributed processing (Spark/Databricks).
- Orchestration and pipeline engineering (e.g., ADF/Databricks workflows), CI/CD and DevOps practices.
- Data governance toolchain exposure desirable (e.g., Unity Catalog / Purview, lineage/catalog concepts, access