Staff Software Engineer - Data Platform Workflow

Requirements

• Deep familiarity with AI-native platforms: Worked closely with MCPs, agents, orchestration layers, skills, or context engineering systems in production. Either as a builder, a contributor, or someone who has shipped meaningful pieces of one.
• Workflow ownership across teams: Owned an end-to-end developer or analyst workflow at scale. Understands where time actually leaks in a multi-step journey and what to optimise first.
• Data platform background: Worked on the engineering side of a data platform. Knows dbt, lineage, modelling layers and BI tools from the inside.
• Cross-team direction setting: Set technical standards or patterns that engineers in adjacent teams adopted by default. Comfortable holding the long-term architecture and the immediate deliverable at the same time.
• Visionary plus pragmatic: Has translated an unsolved problem into a written plan well enough that other engineers could execute against it. Can sit with ambiguity without freezing.
• Adoption mindset: Has shipped a platform feature and measured whether anyone actually used it. Treats adoption as part of the engineering work, not a marketing afterthought.
• Technical knowledge
• AI agents and orchestration: Deep practical experience with MCPs, agent skills frameworks, LLM-based orchestration, and the failure modes that come with them. Production architectural at scale understanding, not casual usage.
• Languages: Strong in Python for platform and tooling work. Comfortable in Go or Java for adjacent systems.
• Data platform stack: Snowflake, dbt, lineage tooling, modern BI tools. Familiar with CI/CD for data pipelines.

Benefits

Additional Information

Join the team redefining how the world experiences design. Hey, g'day, kia ora, 你好, hallo, vítejte! Thanks for stopping by. We know job hunting can be a little time consuming and you're probably keen to find out what's on offer, so we'll get straight to the point. Where and how you can work Our flagship Sydney campus is uniquely Canva - an extension of our Surry Hills neighbourhood. It's a thoughtfully designed space with plenty of room to collaborate, focus, and connect. This role is based in Sydney, and we're looking for someone who calls it home. Our hybrid way of working gives you the flexibility to work remotely, and to come together on campus for meaningful in-person collaboration and connection when it matters most. We trust our Canvanauts to choose the balance that empowers them and their team to achieve their goals. What you'd be doing in this role The Data Platform Workflow team owns the data platform users' analytics journey at Canva. When the workflow is smooth, they ship in minutes. When it isn't, it takes days. The team's job is to keep closing the gap and optimising for a better user experience. The strategic bet is converting that workflow to AI-friendly or AI-native. The speed at which analytics arrives is the speed at which Canva learns. AI generates models faster than humans can write them, surfaces insight earlier in the journey, and changes what review even means. The team has shipped early proof of what this looks like, and we'll need to take it to the next levels. What the team needs you to own is the new type of best practice for an AI-native analytics platform and AI-enabled workflow. The outcome is defined. The shape is open. You'll figure out what that looks like here, sequence the rollout, align the analytics community, and pivot the platform from classic patterns to agent-driven ones. At the moment, this role is focused on: Mapping the journey: Walking the end-to-end path a data platform user takes from question to insights, finding the real bottlenecks, anchoring the team's roadmap in data rather than intuition. Building agent-ready platforms: Converting command-line and human-first interfaces into MCPs, skills, connectors and APIs that AI agents can use natively. BI tooling, modelling layers, lineage. The team has a long list to work through. Setting the standard: Defining what AI-native means inside the data platform, what good looks like, and what other Canva platform teams should be building toward, especially to ensure well governed and reliable AI capabilities at scale. Measuring impact: Building the metrics that show value: adoption, time saved, bottlenecks removed. So the work compounds rather than getting lost. What success looks like: Twelve months in, the data platform workflow at Canva looks materially different from how it looked when this person joined. The biggest bottlenecks identified in the first quarter have been removed or transformed by agent-driven equivalents. Analytics engineers and data scientists are shipping faster. Other platform teams across Infra are referencing this team's work when they think about what agent-native looks like for their own area. You're probably a match We'd love to hear from you if you fit one or more of these. You don't need to meet all of them, but the more the better and if you join the team, we're invested in helping you grow.

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