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Data Architect – Capital Markets


Job Location:

Hong Kong - Hong Kong

Monthly Salary: Not provided by the employer
Posted: 22 September 2026 (Yesterday)
Application Deadline: 20 December 2026
Vacancies: 1 Vacancy

Job Summary

Were looking for a hands-on Data Engineer / Data Solution Architect to join a financial services project in Hong Kong.

The role is ideal for someone who enjoys building data solutions and platforms rather than focusing mainly on day-to-day operations or support.

What were looking for:

  • University degree in information technology computer engineering or related fields
  • At least 7 years of programming and data engineering experience within capital markets orFinTech featuring deep domain knowledge of the front-to-back office trade lifecycle.
  • Advanced proficiency in Java and Python alongside proven expertise in data modellingframeworks and technical writing.
  • Hands-on experience working on Oracle Postgres GaussDB OceanBase NoSQL orObject-Oriented databases.
  • Proficiency working in public and private clouds.
  • Good communication skills to work with user community for analysis and reporting.
  • Candidates must be proficient in English and Cantonese or Mandarin.

Job Responsibilities include:

  • Design scalable real-time and batch ETL pipelinesusing enterprise integration tools. Write optimized production-grade applications in both
    Java and Python.
  • Normalize and cleanse data from multiple sources and buildautomation to enforce and verify the same.
  • Coordinate data movement and schema evolution across a diverse
    matrix including Oracle PostgreSQL GaussDB OceanBase Object Oriented databasesand NoSQL systems.
  • Architect conceptual logical and physical data modelstailored for transactional and analytical systems maintaining meticulous schema
    documentation.
  • Build resilient data solutions within secure cloud environments
    seamlessly parsing and structuring Distributed Ledger Technology (DLT) data.
  • Facilitate data working groups to foster crossdepartmental alignment. Proactively collect technical requirements from non-technicalstakeholders translate business needs into data schemas and provide tier-3 technical
    support for downstream analytics teams.
  • Implement data security access controls and data quality
    monitoring frameworks.