Staff Data Engineer
Senior hands-on data architecture role at Checkbox, a Series A legal-tech SaaS company: own the data and AI reference architecture (context/retrieval layers, vector stores, eventing, tenant-isolated data access) that powers agentic AI products for in-house legal teams, while staying hands-on and mentoring. Reports to the VP of Engineering; hybrid in Sydney CBD.
- Compensation
- Not disclosed
- Location
- Sydney, Australia
- Work setup
- Hybrid
- Seniority
- Staff
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Significant experience as a senior/principal data engineer, data architect, staff engineer or similar technical leadership role
- Proven experience building data and context foundations powering AI products in production
- Strong experience designing data architectures across transactional, operational, analytical and AI-ready systems
- Deep understanding of modern data platform patterns: data contracts, event-driven architecture, ingestion, transformation, observability, lineage, governance
- Strong understanding of AI data patterns: retrieval systems, embeddings, semantic modelling, vector search, knowledge graphs, context engineering, agentic workflows
- Experience with multi-tenant SaaS systems where data segregation, tenancy and access control matter
- Ability to make pragmatic architecture decisions and select the right tool or pattern for each problem
- Strong judgement around buy-vs-build decisions across data infrastructure, retrieval, orchestration and AI platform layers
- Comfortable leading technical direction while still building, reviewing and shipping
- Experience mentoring engineers or leading small technical teams
- Strong communication skills and ability to work with product, platform teams and senior leadership
- Comfortable operating in a fast-moving environment building, scaling and refining systems
Nice to have
- Experience with AI agents, agentic workflows, GenAI platforms or AI-native SaaS products
- Experience designing MCP or API surfaces for data and context access
- Experience with legal tech, workflow automation, enterprise SaaS or document-heavy products
- Experience with AWS-based data and platform infrastructure
- Experience with event-driven systems, queues, Pub/Sub patterns or streaming architectures
- Experience with data security, compliance, auditability and enterprise customer requirements
- Experience growing a small data function into a scalable team
Full description
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 clear 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