Staff Data Engineer

About Checkbox and Role Overview

Checkbox is a Series A stage technology company focused on delivering AI-native SaaS products for in-house legal teams to efficiently manage legal workflows across businesses. The company is pioneering a transformation to agentic-first AI solutions that leverage automation and intelligent workflows. Central to this transformation is developing a sophisticated data platform that acts not just as infrastructure, but as a strong competitive advantage.

Key Responsibilities and Ownership

• Lead the design and ownership of Checkbox’s comprehensive data and AI reference architecture, integrating base data sources, intelligence services, APIs, retrieval layers, and secure data access for AI agents in a multi-tenant environment.
• Develop scalable patterns so product teams can build AI features on common data foundations without reinventing access and management strategies.
• Collaborate closely with Principal Engineers and various product, engineering, AI, and platform teams to establish robust architectural principles encompassing storage, retrieval, compliance, and observability.
• Architect and build context and retrieval subsystems that underpin AI agents and generative AI experiences, making tactical decisions between transactional stores, warehouses, vector search, knowledge graphs, event streams, and API designs.
• Evaluate build-versus-buy options pragmatically for each data layer component, emphasizing effective context delivery with proper control mechanisms around latency, relevance, cost, and compliance.
• Take charge of data platform integrations, eventing, operational and transactional data flow, setting data contracts, event models, ingestion pipelines, and transformation processes scalable across products.
• Ensure data security, tenant isolation, compliance, auditability, and least privilege access guide architecture and implementation choices, working closely with platform security groups.
• Provide technical leadership through mentoring, directing technical efforts, establishing standards and documentation, and promoting operating rhythms that empower the data team’s growth and efficiency.

Expected Outcomes

• Data and AI reference architecture will be tangible, documented, actively maintained, and genuinely embrace multiple storage and serving patterns tailored to use cases.
• Product teams efficiently utilize a secure, compliant, and tenant-aware shared data layer for AI features.
• AI agents reliably consume relevant context with controls ensuring quality, latency, cost-effectiveness, and proper authorization.
• Data governance enforcements like contracts and event models minimize redundant efforts across product lines.
• The data team transitions from a potential bottleneck into a strategic enabler for AI-driven product innovation.
• Checkbox’s data and context capabilities stand out as key strategic advantages.

Candidate Profile

• Extensive senior experience as a data engineer, data architect, principal engineer, or comparable leadership role with substantial hands-on involvement.
• Demonstrated success in designing and deploying data and context frameworks powering AI applications in production environments.
• Proficiency in modern data platform architectures including transactional, operational, analytic, and AI-optimized systems.
• Deep knowledge of event-driven architectures, data contracts, observability, lineage, governance, semantic modeling, retrieval systems, embeddings, vector search, knowledge graphs, and context engineering.
• Experience managing multi-tenant SaaS environments prioritizing data segregation, tenancy, and strict access control.
• Adept at making pragmatic architectural trade-offs, choosing the appropriate tools, and balancing build versus purchase decisions.
• Strong technical leadership and mentoring capabilities, effective communication with product and engineering leadership, and adaptability in fast-evolving contexts.

Additional Valuable Experience

• Work on AI agents, agentic workflows, generative AI platforms, or AI-first SaaS solutions.
• Design experience for API or MCP surfaces for data and context delivery.
• Background in legal technology, workflow automation, enterprise SaaS, or document-centric products.
• Hands-on with AWS data and cloud platform services.
• Familiarity with event-driven systems, streaming architectures, messaging queues, or publish/subscribe patterns.
• Expertise in data security, compliance, audit practices, and enterprise customer requirements.
• Experience developing and scaling data functions from early stage to larger teams.

Benefits and Work Environment

• Competitive remuneration package.
• Hybrid working model with regular team presence at Sydney CBD office.
• Direct reporting to VP of Engineering and significant engineering ownership.
• Opportunities to influence and shape AI and data architecture for innovative agentic products.
• Allocated personal learning and professional development budget.
• Flexible leave and expense policies with salary sacrifice options.
• Access to modern office amenities including refreshments, premium coffee, social events, and a transparent, collaborative culture fostering open feedback.