Data Architect Capital Markets

We're 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 we're 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 or FinTech, featuring deep domain knowledge of the front-to-back office trade lifecycle.
• Advanced proficiency in Java and Python, alongside proven expertise in data modelling frameworks and technical writing.
• Hands-on experience working on Oracle, Postgres, GaussDB, OceanBase, NoSQL, or Object-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 pipelines using enterprise integration tools. Write optimized, production-grade applications in both
  Java and Python.
•  Normalize and cleanse data from multiple sources, and build automation to enforce and verify the same.
• Coordinate data movement and schema evolution across a diverse
  matrix including Oracle, PostgreSQL, GaussDB, OceanBase, Object Oriented databases, and NoSQL systems.
• Architect conceptual, logical, and physical data models tailored 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 cross departmental alignment. Proactively collect technical requirements from non-technical stakeholders, 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.