Principal Data Engineer, Data Asset & Provisioning

Randstad

  • Guangzhou, Guangdong
  • RMB¥1,200,000-1,400,000 per year
  • Permanent
  • Full-time
  • 2 months ago
职位概述about the company.Top 500 global companyabout the team.Global teamresponsibilities: * Define and evolve the reference engineering standards for core data provisioning platforms, ensuring alignment to the bank's Group Data Strategy and Data Future State Architecture.
  • Provide deep technical standards and approach leadership across key platforms:
  • Enterprise Data Assets (EDA) and APIs for standardized data provisioning
  • Reference Data Services for golden source alignment and hierarchy management
  • Big Data and Movement technologies for ingestion, streaming, and transformation at scale
  • Decisioning Infrastructure including real-time feature stores and orchestration
  • Lead peer reviews and ensure platform consistency, reusability, and compliance with enterprise standards.
  • Guide development teams in implementing data pipelines, APIs, metadata-driven controls, and automated testing frameworks.
  • Champion DevSecOps, CI/CD, and infrastructure-as-code across engineering teams.
  • Drive the control transition to using agentic AI coding assistants.
  • Lead the technology Developer Experience, enabling the whole of technology to discover, understand and onboard themselves to our services.
  • Drive best practices in observability, performance engineering, cost optimization, and scalability in hybrid cloud environments.
  • Mentor senior engineers and influence technical delivery across multiple squads and geographies.
  • Collaborate with Architecture, Cybersecurity, CDO, and business product owners to align engineering outcomes to strategic objectives.
  • Represent the domain in cross-platform technology councils and architecture forums.
skills and experience required. * 15+ years of hands-on experience in data platform engineering, architecture, or software development, preferably in a global financial institution or scaled enterprise.
  • Expertise in building large-scale data pipelines, APIs, and event-driven systems using modern frameworks and technologies (e.g., Spring, Kafka, Spark, etc).
  • Strong architectural understanding of data mesh, data lakehouse, and real-time, customer-facing operational & analytics platforms.
  • Experience with cloud platforms (AWS and GCP), containerisation, and IaC tools.
  • Deep familiarity with development tooling and automation covering modeling, observability, resilience, and , and governance patterns.
  • Strong track record of building reusable platform services that accelerate delivery for data consumers and AI/ML initiatives.
  • Experience with enterprise reference data management and integration is highly desirable.
  • Excellent communication and influencing skills, with the ability to engage and guide both engineering teams and non-technical stakeholders.
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Randstad