We're looking for a hands-onData Engineer / Data Solution Architect to join a financial services project in Hong Kong.

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

What we're looking for:

1. University degree in information technology, computer engineering or related fields
2. 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.
3. Advanced proficiency in Java and Python, alongside proven expertise in data modelling frameworks and technical writing.
4. Hands-on experience working on Oracle, Postgres, GaussDB, OceanBase, NoSQL, or Object-Oriented databases.
5. Proficiency working in public and private clouds.
6. Good communication skills to work with user community for analysis and reporting.
7. Candidates must be proficient in English, and Cantonese or Mandarin.

Job Responsibilities include:

1. Design scalable, real-time, and batch ETL pipelines using enterprise integration tools. Write optimized, production-grade applications in both
2. Java and Python.
3. Normalize and cleanse data from multiple sources, and build automation to enforce and verify the same.
4. Coordinate data movement and schema evolution across a diverse
5. matrix including Oracle, PostgreSQL, GaussDB, OceanBase, Object Oriented databases, and NoSQL systems.
6. Architect conceptual, logical, and physical data models tailored for transactional and analytical systems, maintaining meticulous schema
7. documentation.
8. Build resilient data solutions within secure cloud environments,
9. seamlessly parsing and structuring Distributed Ledger Technology (DLT) data.
10. 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
11. support for downstream analytics teams.
12. Implement data security, access controls, and data quality
13. monitoring frameworks.