Must have

• Data engineering

• Data modeling

• ETL

• Big Data

• Spark

• Scala

• Python

• HDFS

• Hive

• Cloud

• Google Cloud

• AI

• MLOps

• Microservices architecture

• PUB

• Tableau

• Qlik

• BI

• UML

• English

Nice to have

• TOGAF

• NoSQL

• Stakeholder management

Requirements description

What we expect from you:

• 8+ years of experience in Data Engineering / Solution Architecture.
• Strong experience with data modeling (logical, physical), data warehousing (Kimball/Inmon), ETL, and non-functional design.
• Hands-on experience with Big Data technologies: Spark, Scala/Python, HDFS, Hive.
• Public cloud expertise (Google Cloud preferred), especially around data processing & architecture.
• Familiarity with AI/GenAI tools, MLOps, and OpenAI/Vertex AI services.
• Knowledge of microservices architecture, APIs, Pub/Sub, Kubernetes-hosted platforms.
• Tools: Erwin/IDA for modeling, Tableau/Qlik for BI, UML/Archimate/EA for design.

Nice to have:

• Experience with architecture frameworks (TOGAF, IFW), metadata management, unstructured data systems (NoSQL, graph DB).
• Financial services/banking experience and stakeholder management in global setups.
• Fluent English (Mandarin is a plus).

Offer description

We are looking for an experienced Senior Solution Architect to join an international financial technology team and take ownership of designing scalable and reliable data solutions used across global markets.

Be part of a globally impactful program shaping how data powers decision-making at one of the world's largest institutions.

Your responsibilities

1. Lead the design and architecture of end-to-end data and platform solutions aligned with enterprise standards.
2. Translate business requirements into actionable data and solution designs.
3. Ensure all technical designs are scalable, cost-effective, and fit for purpose.
4. Collaborate with DevOps, product owners, and technical teams across regions.
5. Oversee governance, documentation, architecture reviews and technical assurance.
6. Design and optimize architectures involving structured and unstructured data, APIs, streaming, and ML/GenAI integration.