Staff Software Engineer, Essentials (Calgary)
Staff engineer on Syndio's Essentials pay-equity product: optimizing high-performance statistical data pipelines (Postgres, Redis, queues, orchestration) so they scale to huge customer datasets, and building AI-agent tooling, harnesses, and evaluation so agents can reliably run Essentials workflows. Stack includes Python, Go, TypeScript, React, and Postgres.
- Compensation
- CA$180K–CA$200K / yr
- Location
- Calgary, Alberta, Canada
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 8+ years of professional hands-on experience as a staff-level engineer
- Deep experience in complex, distributed, service-based systems
- Proven performance and scale work: profiling, finding bottlenecks, large-dataset/numerical/statistical workloads without breaking correctness
- Experience building workflows on message systems, queues, or workflow orchestration tooling
- Strong across the stack: Python, Go, Node/JavaScript, TypeScript, React, Postgres, NoSQL stores such as Firebase
- Familiarity with caching layers like Redis and their tradeoffs
- Strong end-to-end and integration testing of service-based architectures, hands-on with an e2e framework (Playwright, Cypress, or similar)
- Strong agentic development practice: daily coding-agent use, harnesses, tight test loops, CI/CD gates
- High autonomy: drive ambiguous, high-stakes problems to shipped results
- Cross-team collaboration, including with non-technical leaders
- Best-practice leadership: setting and living by team standards
- Based in Calgary and legally authorized to work in Alberta without additional visa support now or in the future
Nice to have
- Hands-on experience building with AI systems: LLM applications, agent frameworks, retrieval, evaluation harnesses, or model-backed pipelines in production
- Background in statistics, data science, or quantitative analysis — regression modeling in particular
- Experience in a domain where correctness is non-negotiable
- Experience working in a large, complex codebase and making it more navigable
Full description
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.