Checkbox is a Series A technology company building AI-native SaaS for in-house legal teams. Our platform helps legal teams manage how work is raised, understood, routed, actioned and resolved across the business.

We are now transforming our SaaS ecosystem into agentic-first products, using AI agents and intelligent workflow automation to change how legal work gets done. To support this, we are investing deeply in the data, context and retrieval foundations that power reliable AI product experiences.

This is a critical next chapter for Checkbox. Our data platform needs to become more than infrastructure. It needs to become a competitive moat.

The Role

We are looking for a Staff Data Engineer / Data Architect to own Checkbox’s data strategy and the architecture that delivers it.

This is a senior technical role reporting directly to the VP of Engineering. You will partner with 1 other Staff Data Engineer, and remain deeply hands‑on in the design and delivery of the systems you define.

The Data team exists to serve the product engineering streams building AI agents, so close day‑to‑day partnership with Product, Engineering, AI and Platform teams is central to the role.

You will own the data and AI reference architecture that underpins our agentic product direction. This includes base data sources, intelligence services, context and retrieval layers, API and MCP surfaces, and the secure data access patterns required to give AI agents compliant, tenant‑isolated context.

This is not a role for someone who only wants to design from a distance. We need someone who has built data and context foundations for AI products in production, can make pragmatic architecture decisions, and can lead by building.





What You’ll Own

Data and AI Reference Architecture

- Own the data and AI reference architecture across Checkbox’s product ecosystem
- Define how base data sources, intelligence services, APIs, MCP surfaces, context engines and retrieval layers fit together
- Design patterns that allow product engineering streams to build AI features on shared foundations rather than reinventing data access each time
- Establish explicit architectural principles for storage, retrieval, serving, tenancy, observability and compliance
- Work hand in glove with the Principal Engineer on architecture that spans application, platform, eventing, data and AI systems

Context, Retrieval and AI Data Foundations

- Architect and build the context and retrieval layers that power AI agents and GenAI product experiences
- Design secure, tenant‑isolated data access patterns for AI systems
- Define how customer, matter, workflow, document, event and operational data should be modelled, retrieved and served
- Evaluate and select the right data patterns for each problem, including transactional stores, operational data stores, warehouses, vector stores, semantic layers, knowledge graphs, event streams and APIs
- Make buy‑vs‑build decisions layer by layer rather than defaulting everything to in‑house development




- Ensure AI agents receive the right context at the right time, with appropriate controls around access, relevance, latency, cost and compliance

Data Platform, Integrations and Eventing

- Own architecture across integrations, eventing, operational data, transactional data and AI‑ready data
- Define data contracts, event models, ingestion patterns and transformation approaches that scale across multiple products
- Partner with product engineering teams to ensure new product capabilities generate usable, reliable and well‑governed data
- Build shared data capabilities that support analytics, AI agents, workflow automation and customer‑facing product experiences
- Improve data quality, lineage, observability and reliability across the data lifecycle

Security, Tenancy and Compliance

- Treat security, tenancy and data isolation as first‑order architectural concerns
- Ensure data access patterns are compliant, auditable and appropriate for enterprise customers
- Design systems that support tenant isolation, data segregation, least privilege access and secure retrieval
- Partner with Platform, Security and Engineering teams on encryption, decryption, access control, auditability and production readiness
- Ensure data and context systems can support customer trust, compliance requirements and future audit needs

Team Leadership and Technical Direction

- Set technical direction while staying close to implementation
- Partner with 1 other Staff Engineer in our Greenfield Data Platform, sharing the load across Analytics, Warehousing, AI & ML.
- Mentor engineers on data architecture, AI data foundations, retrieval patterns and pragmat

📌 Staff Data Engineer (Sydney)
🏢 Checkbox
📍 Sydney