Director, Data and Knowledge Platform Engineering

Airwallex•San Francisco, CA

3d•$300,000 - $330,000•Onsite

About The Position

We’re hiring a Director, Data and Knowledge Platform Engineering (based in San Francisco) to own the architecture, delivery, and adoption of a governed, reusable Data → Knowledge → Skills stack that powers analytics, AI agents, and real-time decisioning across the company. This is a product-facing platform leadership role. The successful leader will design and scale the semantic and operational layers on top of our Data Lakehouse (Databricks), enforce strong governance and lineage, and productize capabilities so both human analysts and machine agents reason and act consistently. Our platform is evolving beyond raw data storage into a structured, scalable system comprising three tightly integrated layers: Data – Governed, well-modeled, structured datasets including transactional records, event streams, dimensional models, and feature tables. This foundation must deliver high quality, strong lineage, and built-in regional compliance. It should be clean, trusted, and fully queryable. Knowledge – A semantic layer that makes data meaningful to both humans and machines. This includes standardized business metric definitions, entity relationships, contextual documentation, and company-specific domain knowledge. It enables analysts and AI agents alike to ask questions such as “What is our net revenue retention in SEA?” and receive consistent, trustworthy answers without having to reverse-engineer underlying schemas. Skills – Operationalized capabilities that act on data and knowledge. These include reusable analytical workflows, agent-callable tools, automated pipelines, API endpoints, and transformation primitives. Together, they form the building blocks that allow AI agents and internal teams not only to query information but to reason, automate, and execute reliably. We are looking for a leader who can architect and scale this governed stack across regions and use cases, while ensuring it remains accessible and intuitive for Data Science, AI engineering, and technical business stakeholders. The objective is to create a shared foundation that enables both human analysts and AI agents to reason consistently and act confidently. A critical component of this role is ownership of the regional and global data localization strategy within our Databricks environment. This includes designing architectural patterns that satisfy regulatory requirements while preserving a unified global data model across priority markets. The platform must reconcile local compliance constraints with global consistency in definitions, metrics, and knowledge artifacts. You will partner closely with Data Science, AI engineering, Product, and Risk teams to ensure the platform supports rapid experimentation, intelligent automation, and high-quality decision-making, transforming raw data into durable knowledge and deployable skills across the organization.

Requirements

15+ years in data or platform engineering
5+ years leading engineering teams in complex, multi-region environments.
Proven experience architecting and scaling modern data platforms that extend beyond storage to include semantic layers, metadata systems, or knowledge graphs.
Strong hands-on expertise in data modeling (transactional, event-driven, dimensional, feature tables) with robust data quality, lineage, and observability practices.
Experience designing and operationalizing a governed semantic layer with standardized, auditable business metric definitions.
Track record of building reusable, programmatic capabilities on top of data platforms (APIs, orchestration frameworks, analytical workflows, agent-callable tools).
Deep familiarity with modern lakehouse architectures, particularly within a Databricks environment.
Experience implementing regional data localization and residency strategies while maintaining a unified global data model.
Strong understanding of data governance, privacy, and regulatory controls in multi-jurisdictional contexts.
Platform-as-product mindset, with experience driving adoption, improving developer experience, and operating with clear SLAs/SLOs and measurable impact.

Responsibilities

Own the architecture, delivery, and adoption of a governed, reusable Data → Knowledge → Skills stack.
Design and scale the semantic and operational layers on top of our Data Lakehouse (Databricks).
Enforce strong governance and lineage.
Productize capabilities so both human analysts and machine agents reason and act consistently.
Architect and scale the governed stack across regions and use cases.
Ensure the platform remains accessible and intuitive for Data Science, AI engineering, and technical business stakeholders.
Create a shared foundation that enables both human analysts and AI agents to reason consistently and act confidently.
Own the regional and global data localization strategy within our Databricks environment.
Design architectural patterns that satisfy regulatory requirements while preserving a unified global data model across priority markets.
Reconcile local compliance constraints with global consistency in definitions, metrics, and knowledge artifacts.
Partner closely with Data Science, AI engineering, Product, and Risk teams to ensure the platform supports rapid experimentation, intelligent automation, and high-quality decision-making.
Transform raw data into durable knowledge and deployable skills across the organization.