Join the team redefining how the world experiences design.
We know job hunting can be time consuming, 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 hosts 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. Our hybrid way of working gives you the flexibility to work remotely and to come together on campus for meaningful in‐person collaboration when it matters most.
What you'd be doing in this role
The Data Sync team owns the data‐movement layer of Canva's platform. Every first‐party event a user generates and every third‐party signal that flows in from external partners runs through pipelines the team builds. Every data egress to downstream platforms runs through here too. CDC, snapshots, streaming, batch – the whole movement layer. The volume is real and growing fast.
Our strategic bet is moving from "land then transform" to streaming‐native, AI‐aware data movement. Data movement is the layer every product, ML, and analytics team builds on, and the speed at which data moves determines what those teams can ship. The team launched a Flink‐based streaming pattern earlier this year together with a lakehouse architecture on S3 with Iceberg tables. The transformation happens in flight, faster, cheaper, closer to real time. Early returns are significant, with several million dollars a year in projected savings.
What the team needs you to own is the technical vision for streaming‐native data movement. The outcome is defined. The technical path is open. You'll decide what consolidates and what stays, what the migration sequence looks like, and what new patterns AI agents need now that the downstream consumer is increasingly an agent rather than a dashboard. The architectural calls made here become how Canva moves data for years.
Responsibilities
* Own configuration frameworks. The team's core configuration frameworks and abstraction tools are the substrate every downstream consumer touches. Evolving them is the central technical job.
* Ship the streaming modernisation. Flink, Kinesis, outbox patterns, Iceberg. Get the modernisation goal across the line without destabilising the product, ML and analytics teams that depend on it.
* Define paved roads for data movement. What does a self‐service ingestion pipeline look like at Canva's scale? What does a self‐service egress pipeline look like? Build the patterns other teams adopt rather than reinvent.
* Consult across teams. Sit with product, ML and analytics teams to map their data movement needs, then shape the platform so they build less themselves. Internal developer advocacy is part of the job.
* Co‐design the team roadmap. Work alongside the engineering lead to shape what the team takes on next, what it deprecates, and what good looks like twelve months out.
* Lifting the craft level. Mentor the existing IC team on platform engineering practice through code review, design feedback and how the team handles its hardest decisions.
* Compound patterns. One well‐built pipeline pattern here becomes how a dozen downstream teams ship data tomorrow. Small team, org‐wide leverage.
Success metrics
* Within twelve months, the technical standards for data movement at Canva are set. Engineering effectiveness across the Data Platform group is measurably higher than when this person joined.
* The streaming modernisation goal has landed, with downstream consumers either on the new pattern or with a credible migration path.
* This person is the trusted consultant across teams on Data Sync craft. The engineer other groups call when they're designing something data‐shaped.
You're probably a match if
* Built a platform multiple teams used: Own a piece of platform infrastructure across multiple consuming teams in a company of comparable scale. Not single‐team delivery. Actual platform leverage.
* Configuration frameworks or abstraction tooling: Shipped configuration frameworks or abstraction layers that other engineers adopted by default. Knows where abstractions help and where they leak.
* Data movement at scale: Production experience with sync, CDC, replication, or warehouse pipelines. End‐to‐end ownership from design through to running it in production.
* Cross‐team consulting that landed: Worked with engineers in other teams to shape their decisions without formal authority. Comfortable holding the long‐term architecture and the immediate deliverable at the same time.
* Set technical standards that stuck: Defined a pattern, a framework or a way of working that other teams adopted as their default. Bonus for measurable adoption.
* Software engineering depth, not data engineering: This role is seventy to eighty percent hands‐on SWE. Strong as a backend engineer first, data platform second. Pure DE or analytics backgrounds aren't the right fit here.
Technical knowledge
* Languages: Strong in Python or Go (Java acceptable if it's the strongest language). Most day‐to‐day work on this team is Python and Go.
* Streaming: Practical depth in Kafka, Kinesis, AWS DMS or Flink. Strong opinions on exactly‐once, at‐least‐once, and the operational tail of each.
* Warehouse and lakehouse: Snowflake, Iceberg, S3 storage layouts. Comfortable reasoning about cost, query performance and schema evolution at scale.
* CS fundamentals: Concurrency, multithreading, distributed systems primitives, failure handling. Strong on architecture and design patterns.
* AWS as primary cloud: Comfortable with the trade‐offs across managed services, self‐managed equivalents, and where to draw the line.
* Infrastructure as code: Terraform fluency.
* AI‐assisted engineering: Daily user of Copilot, Claude Code or equivalent at the senior engineer level. Explicitly listed as a hiring criterion for this role.
Nice to have
* GCP experience: On top of AWS. The team's secondary cloud and increasingly relevant.
* Configuration framework design: Open‐source or production work on configuration tooling, schema evolution, or infrastructure abstraction.
* Developer experience wins: Documented improvements to onboarding time, build time, or platform toil for engineers in another team.
* Open‐source data tooling contributions: Flink, Iceberg, Kafka Connect, dbt or equivalents. Even small contributions signal genuine depth.
The Core Sync 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. Data Platform sits within Infra and owns the platform and infrastructure of Canva's data storage and analytics warehouse.
The Data Sync team is the data‐movement layer that makes this work. A focused team with strong IC depth and a clear technical anchor missing. The senior strategic role between coding and leadership. The team works in Python and Java, with Kafka, SQS, Snowflake, AWS DMS and Kinesis. Mission: provide secure, efficient and cost‐effective means to integrate data with the warehouse. Vision: empower users to manage data ingestion or exports by building paved roads that are secure and efficient. The work is depended on by every product, ML and analytics team at Canva. The leverage isn't this team's own output; it's the velocity of every team that builds on top.
What's in it for you?
* 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 support you personally.
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