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Airwallex

Data Platform Principal Architect

Airwallex is hiring a Data Platform Principal Architect in Singapore, Singapore. Level rates it ; you can apply on Airwallex.

Level's read on this job

  • On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
  • Tools and skills the posting cites include Databricks, RAG and AI agents.
  • Level classifies it as a lead role.
  • Level lists 507 other open roles at Airwallex.

AI in this role

databricks
ragai-agents

About Airwallex

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.

We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.

We're co-headquartered in San Francisco and Singapore, with more than 2,700 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles to see it in full.

 

About the team

The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. We accomplish this by creating platforms that handle the entire data and AI/ML lifecycle, simplifying the interface while providing proper safety and governance. This includes managing our data infrastructure (Databricks, Spark, Kafka, etc.), the technology to serve that data to our users (RAG, MCP, etc.), and the platform to host and govern these AI/ML models.

In 2026, our team’s overarching mission is to evolve our full data ecosystem—encompassing both platform and models—into a fully AI agent-ready infrastructure; we will empower customers to engage directly with the data platform to extract actionable value through capabilities like analytics and natural language querying, while also upgrading the platform to deliver robust, real-time performance for instant, data-driven decision-making.

How you'll make impact

You’ll architect the governed, reusable data, knowledge, and skills layers that power analytics, AI agents, and real-time decision systems across Airwallex. You’ll define scalable patterns for structured datasets, semantic models, business metrics, entity relationships, contextual documentation, analytical workflows, agent-callable tools, and transformation primitives.

  • Own the technical execution of regional and global data localisation strategies within Databricks, balancing regulatory requirements with a unified global data model across priority markets.

  • Design scalable patterns for governed datasets, semantic models, knowledge artifacts, analytical workflows, agent-callable tools, and transformation primitives.

  • Reconcile local compliance constraints with global consistency in data definitions, metrics, and knowledge assets.

  • Partner with Data Science, AI Engineering, Product, and Risk teams to support experimentation, high-quality decision-making, and reliable AI-enabled workflows.

  • Lead global data modelling efforts, define Agent Interfaces through APIs and tools, and embed automated lineage and quality controls through Governance-as-Code.

  • Serve as the primary technical authority as the team’s most senior individual contributor, leading design reviews and mentoring engineers.

You'll work from our Singapore office five days a week.

What we're looking for

Essentials

  • 10+ years of experience designing and building large-scale data systems with strong architectural fundamentals.

  • Hands-on experience with the Databricks ecosystem, including Unity Catalog and Delta Lake.

  • Experience implementing semantic layers or metric stores to enable consistent reporting and analytics.

  • Strong understanding of how AI and agent-based systems consume data, with experience building APIs or data services to support them.

  • Experience designing data solutions that meet regional data residency and compliance requirements.

  • Demonstrated senior-level individual contributor experience, including leading design reviews and mentoring engineers.

You'll thrive here if

  • You're comfortable with ambiguity. Give you a problem, not a prescription, and you'll run with it.

  • You own the outcome and you don't wait for permission to fix what's broken.

  • You connect dots across functions and hold the big picture without losing grip on the details.

  • You simplify complexity rather than hiding behind it.

  • You’re comfortable operating across time zones with a globally distributed team, including colleagues based in APAC and EMEA.

Learn more about your team

The Knowledge Platform sits at the foundation of Airwallex’s Data and AI strategy. We are evolving traditional data infrastructure into knowledge infrastructure—turning structured and unstructured information into trusted, reusable context that helps people and AI systems make better decisions and take action.

You’ll build the platform end to end, from core technologies such as Databricks, Clickhouse, Spark, and Kafka to real-time analytics, knowledge retrieval and indexing, model infrastructure, agent capabilities, and AI interaction history. You’ll also develop the evaluation, annotation, feedback, governance, permission, and freshness frameworks required to operate AI reliably at global financial scale.

Our mission is making this ecosystem truly agent-ready. That means enabling teams to explore data through natural language, investigate business drivers in real time, and give AI systems secure access to the context and capabilities they need to support decisions and execute workflows.

If you want to build the foundation that turns Airwallex’s data into knowledge, knowledge into intelligence, and intelligence into measurable business impact, this is where that work happens.

Applicant Safety Policy: Fraud and Third-Party Recruiters

To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page.

Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.

Equal opportunity

Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.

How we rate this

Data Platform Principal Architect at Airwallex rates 64 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsDatabricks

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. What are the limits of Databricks that you've run into, and how did you work around them?
  4. Describe a typical day in a role like this one: which parts run through AI directly?
  5. If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, and Databricks. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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