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About the Role

Checkbox is seeking a Staff Data Engineer / Data Architect to own the company's data strategy and architecture. This role is critical for building the data foundations that power reliable AI product experiences and will involve hands-on design and delivery of data systems. You will partner closely with Product, Engineering, AI, and Platform teams to define the data and AI reference architecture for agentic products.

Responsibilities

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.
Establish clear architectural principles for storage, retrieval, serving, tenancy, observability and compliance.
Work closely with the Principal Engineer on architecture spanning application, platform, eventing, data and AI systems.
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.
Make buy-vs-build decisions layer by layer.
Ensure AI agents receive the right context at the right time with appropriate controls.
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.
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.
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.
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 pragmatic systems design.
Build operating rhythms, standards and documentation that help the Data team scale.
Act as the senior technical voice for data architecture across engineering leadership discussions.

Requirements

Significant experience as a senior data engineer, principal data engineer, data architect, staff engineer or similar technical leadership role.
Proven experience building data and context foundations that power AI products in production.
Strong experience designing data architectures across transactional, operational, analytical and AI-ready systems.
Deep understanding of modern data platform patterns, including data contracts, event-driven architecture, ingestion, transformation, observability, lineage and governance.
Strong understanding of AI data patterns such as retrieval systems, embeddings, semantic modelling, vector search, knowledge graphs, context engineering or agentic workflows.
Experience working 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 closely with product engineering streams, platform teams and senior engineering leadership.
Comfortable operating in a fast-moving environment where systems are being built, scaled and refined at the same time.

Skills

Data strategy
Data architecture
AI data foundations
Retrieval patterns
Pragmatic systems design
Data contracts
Event-driven architecture
Ingestion
Transformation
Observability
Lineage
Governance
Retrieval systems
Embeddings
Semantic modelling
Vector search
Knowledge graphs
Context engineering
Agentic workflows
Multi-tenant SaaS systems
Data segregation
Tenancy
Access control
Buy-vs-build decisions
Data infrastructure
Orchestration
AI platform layers
Encryption
Decryption
Auditability
Production readiness
MCP surfaces
API surfaces
AWS-based data and platform infrastructure
Event-driven systems
Queues
Pub/Sub patterns
Streaming architectures
Data security
Compliance
Enterprise customer requirements
Legal tech
Workflow automation
Enterprise SaaS
Document-heavy products

Location

Sydney

Work Type

Full-time
Hybrid

Experience Level

Staff
Senior
Principal

Benefits

Competitive salary
Personal learning and development budget
Flexible leave policy
Expense policy and salary sacrifice options
CBD start-up hub with snacks, drinks, premium coffee and team socials
Company-wide social events and annual off-sites

About the Company

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.