Staff Software Engineer, Essentials (Calgary)

Do you want to empower organizations to build smarter compensation strategies while ensuring fair pay for all employees?

Syndio is the leading pay governance and compensation intelligence platform. We help organizations make better pay decisions at every stage of the compensation lifecycle, from leveling and offers to promotions and merit. Our platform gives HR, compensation, and finance leaders the data and decision support they need to govern pay fairly, compliantly, and with confidence. We partner with many of the world’s most recognized and respected enterprises, helping them implement leading-edge compensation solutions with expert guidance and analyzing pay for over 10 million employees across the world.

Join us in our mission to help companies make smarter pay decisions they can trust!

About the Role

Essentials is our pay equity product, and its analyses are relied on for high-stakes decisions. Delivering that means running complex statistical work across datasets that routinely reach hundreds of thousands of records, fast enough that a customer can iterate on a real question instead of waiting overnight for a job to finish.

This role is for the engineer who makes that computation fast, scalable, and trustworthy. You'll work on the workflows that move large datasets through ingestion, validation, modeling, and reporting: making them performant, making them observable, making them resilient when a single customer's dataset is an order of magnitude larger than anyone planned for. The architecture is genuinely complex, and you'll be one of the people who makes it more tractable rather than less.

The second half of this role is agentic. We are investing heavily in AI agents across the company, and Essentials is one of the highest-leverage places to apply them. You'll build the tooling that lets our agents carry out Essentials workflows — the harnesses, the guardrails, the context, and the evaluation that make an agent's output something we can stand behind, building on the AI capabilities our AI team provides.

This is a Staff role, sitting one level below Principal, and it carries the scope and expectations that implies. Success is measured two ways: how much larger and more complex a dataset we can handle without degrading, and how much of the Essentials workflow our agents can carry reliably.

What You'll Own

High-Performance Data Workflows

• Build and optimize workflows that run complex mathematical and statistical calculations over datasets of hundreds of thousands of records — and design them so the next order of magnitude doesn't require a rewrite.

• Own performance end to end: query plans and indexing in Postgres, batching and parallelism, memory behavior, caching strategy with Redis, and the difference between an algorithm that's clever and one that's actually fast on real customer data.

• Make correctness verifiable. These results carry real consequences for our customers — performance work that quietly changes an output is worse than no performance work at all.

Orchestration & Scale

• Work with the technologies that make large workflows dependable: message and queueing systems, workflow orchestration, job scheduling, retries, idempotency, and backpressure.

• Design for partial failure. Long-running multi-stage pipelines fail in the middle; the interesting engineering is in what happens next.

• Build the observability that makes a slow or wrong run diagnosable — timing, lineage, and intermediate state you can actually inspect.

Agentic Tooling for Essentials

• Build the tooling that lets our agents execute Essentials workflows: data validation and remediation, analysis setup, exception triage, report generation, and the long tail of work that currently requires a human to babysit it.

• Design the harnesses that make agent output trustworthy in a domain where being confidently wrong is unacceptable — evaluation sets, verification steps, constrained tool surfaces, and clear human review points.

• Give agents the context they need to be productive across a large codebase: documentation, schemas, conventions, and project state that stay current.

• Continuously sharpen your own agentic development workflow, and turn what works into practices the rest of the team adopts.

Raising the Bar on the Essentials Team

• Partner with the Engineering Manager and technical leaders to set standards for testing, performance, and agentic practice — and hold the line on them.

• Improve the testing story for a complex service-based architecture: integration and end-to-end coverage that gives engineers and agents fast, honest feedback.

• Mentor through the work — design review, pairing, and code review that leaves the team stronger.

What We're Looking For

• Staff-level engineer. A high-output individual contributor who raises the output of everyone around you with 8+ years of professional hands-on experience.

• Deep experience in complex, distributed, service-based systems — you're comfortable reasoning about failure modes, data consistency, and latency across service boundaries, not just within one service.

• Proven performance and scale work. You've made something big go fast: profiled it, found the real bottleneck, and fixed it without breaking correctness. Experience with large-dataset computation, numerical or statistical workloads, or heavy data pipelines is exactly what we're after.

• Orchestration experience. You've built workflows on message systems, queues, or workflow orchestration tooling, and you know why those abstractions exist.

• Strong across our stack. Python, Go, Node/JavaScript, TypeScript, React, Postgres, and NoSQL stores such as Firebase. You don't need all of them on day one, but you should be genuinely strong in several and unafraid of the rest. Familiarity with caching layers like Redis and the tradeoffs they bring.

• Testing as a first-class skill. Strong end-to-end and integration testing of service-based architectures, with hands-on experience in an e2e framework (Playwright, Cypress, or similar) and a point of view on how to test systems where the interesting bugs live between the services.

• A strong agentic development practice. You use coding agents daily, you tune how you use them, and you build the harnesses that make them reliable — tight test loops, strong CI/CD gates, spec-to-code workflows, and ways of working that produce high-quality output rather than plausible-looking output.

• High autonomy. You can take an ambiguous, high-stakes problem and drive it to a shipped result without being managed through it.

• Cross-team collaboration. You work well across teams and with non-technical leaders, and you can translate technical constraints into business language without dumbing it down.

• Best-practice leadership. You partner with technical leaders to establish standards across the team, and then live by them publicly.

Nice to Have

• Hands-on experience building with AI systems — LLM applications, agent frameworks, retrieval, evaluation harnesses, or model-backed pipelines in production. This is a strong asset for this role.

• Background in statistics, data science, or quantitative analysis — regression modeling in particular.

• Experience in a domain where correctness is non-negotiable and a wrong number carries real consequences.

• Experience working in a large, complex codebase and making it more navigable.

About You

• You like building products people depend on, and you care about the experience of whoever's on the other end — not just whether the pipeline ran.

• You've worked with sensitive data and treat security, privacy, and compliance as part of your job rather than someone else's.

• You've seen customer onboarding up close and know the ways cross-system data flows go wrong in practice.

• You're comfortable in unfamiliar territory — reading a third-party API's docs, tracing a bad record back through a pipeline, and working out what actually happe