Back End Engineer

See what you can do with Prophix

At Prophix, we’re building the platform that helps finance teams stop managing spreadsheets and start driving strategy. Prophix One™ brings planning, reporting, consolidation, and automation together in a single place, and we are expanding its AI capabilities faster than ever. If you want to work on a product that genuinely changes how finance teams operate, and do it alongside people who care about getting it right, this is where you want to be.

We’re headquartered in the Greater Toronto Area, with teams and offices across North America, Europe, and Australia. Trusted by more than 3,000 finance teams across 100+ countries, Prophix One™ is built for organisations that want to plan smarter and move faster.

At Prophix, the OLAP cube engine sitting at the heart of FP&A Plus is what makes every budget, report, and multi-dimensional calculation possible for more than 3,000 finance teams worldwide. As a Senior Back-End Engineer specializing in Java and OLAP, you will own the Java side of that engine — designing, optimizing, and scaling the cube capabilities that our customers depend on every day. You will work in close partnership with the .NET Cube module team to keep the engine and its consumers in lockstep, ensuring correctness and performance hold up as customer models grow in complexity and scale.

Requirements

What You Will Do

• Design, build, and optimize OLAP cube capabilities in Java and Atoti, including dimension and hierarchy modeling (alternate and parent-child hierarchies), calculated measures, and aggregation performance across large, real-world customer data volumes.
• Own the performance-critical path of cube operations — member selection, calculation over large hierarchies, and data write/merge paths — where both correctness and low latency are non-negotiable.
• Partner closely with the .NET Cube module team on dimension and fact conversion, cube data merging, and cache strategy, keeping the Java engine and its .NET consumer aligned as cube contracts evolve.
• Investigate and resolve OLAP-specific defects, including hierarchy resolution edge cases, aggregation correctness issues (such as zero-denominator and boundary-value handling), and member-property semantics that must match customer expectations exactly.
• Profile and tune cube query and calculation performance as models scale in dimension count, member count, and hierarchy depth, continuously pushing the performance envelope under production conditions.
• Define and evolve the contract between the cube engine and its .NET/Postgres-backed consumers — including response shapes, versioning, and backward compatibility — ensuring existing consumers are never broken across releases.