Staff Software Engineer - Data Platform Workflow

Job Description

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

Collingwood is home to our Melbourne campus - a vibrant, creative hub for connection and impactful work. While Sydney is home to our HQ, Melbourne brings its own unique vibe, with local artwork, lush greenery, and thoughtfully designed spaces to help you collaborate, focus, and feel part of a welcoming community.

This role is based in Melbourne, 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.

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.

Experience

• 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.

• API and connector design: Can build interfaces that humans and agents both use well. Often the same underlying system with different shapes on top.

• CS fundamentals: Concurrency, system design, failure handling, observability. Strong on architecture and design patterns.

• Cloud primary: AWS depth required (GCP a bonus). Understands the cost and reliability trade-offs of managed services at scale.

• AI-assisted engineering: Daily user of Copilot, Claude Code, or equivalent at the architectural level.

Nice to have

• Open-source agentic work: Contributions to MCP servers, agent frameworks, or LLM evaluation tooling.

• Snowflake or dbt depth at scale: Anyone who has run dbt or Snowflake for a five-thousand-person company knows things that don't appear in documentation.

• Migration credibility: Led a migration where the old way and the new way had to coexist for months, and the team didn't lose velocity in the middle.

• Adjacent platform contributions: Built a developer experience improvement that engineers in another team noticed without being told to.

About the Group and Team

Join the Infra supergroup at Canva, where our mission is to build the systems that 5,000 engineers and 225 million users rely on every day. Cloud infrastructure, developer tooling, data pipelines, security, quality automation. Infra's four pillars: keep Canva secure, keep Canva resilient, accelerate and empower Canvanauts, simple and efficient at scale.

Data Platform sits within Infra and owns the platform and infrastructure of Canva’s data storage and analytics warehouse. The group's working mission: to build a world-class platform that empowers Canvanauts to self-serve and unlock value from data with a delightful experience. The group has moved from analytics focused to platform focused and taken on transactional databases on top.

The Data Platform Workflow team is the layer between the data platform and the people who use it. Analytics engineers, data scientists, every Canvanaut who builds a dashboard or ships a model. The team is small and the leverage is large. The room to redefine what a five-thousand-person analytics platform should look like is real.

What’s in it for you?

Achieving our crazy big goals motivates us to work hard — and we do — but you'll experience lots of moments of magic, connectivity and fun woven throughout life at Canva, too. We also offer a range of benefits to set you up for every success in and outside of work.

Here's a taste of what's on offer:

• Equity packages - we want our success to be yours too

• Inclusive parental leave policy that supports all parents & carers

• An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more

• Flexible leave options that empower you to be a force for good, take time to recharge and supports you personally

Other stuff to know

We see AI as a powerful amplifier of creativity and technology at Canva. We're evolving how we assess AI skills in our Technology hiring experience — you'll tackle interactive, real-time challenges that reflect the kind of work we do. In some interviews, you may also be asked to solve a problem using an AI tool to show how you approach challenges with tech by your side.

We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture.

When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. We celebrate all types of skills and backgrounds at Canva, so even if you don't feel like your skills quite match what's listed above — we still want to hear from you!

Please note that interviews are conducted virtually.