Principal Data Developer

Perceptyx•Toronto, ON

13d•CA$150,000 - CA$190,000•Remote

About The Position

At Perceptyx, we’re on a mission to help organizations turn employee insights into measurable impact. Our AI-enabled employee experience platform leverages the science of listening, behavioral nudges, and predictive analytics to help clients elevate engagement, retention, and performance. We’re a team of innovators fueled by purpose, collaboration, and excellence — and we’re looking for someone who wants to play at the highest level as we expand our new Toronto team. We are looking for data engineers to build the data infrastructure that powers our mission of transforming how organizations understand and improve the Employee Experience. As one of the founding members of our new Toronto hub, this is a mid-to-senior role designed for someone who wants to build, not maintain — to design systems from scratch, set the standards, and watch other teams come to depend on what they ship. You’ll thrive here if you do your best work with true ownership and ambiguity rather than a fully-specified ticket, treating your data pipelines as products and your internal consumers as customers.

Requirements

4+ years of experience building and operating production-grade data pipelines that cross-functional teams depend on.
Strong Python programming skills with a demonstrated ability to write clean, maintainable, and testable code.
Expert-level SQL and data modeling instincts. You can architect an enterprise warehouse layer from scratch and clearly articulate the why behind your schema design choices.
Hands-on experience with cloud data warehouses (Snowflake, BigQuery, or similar) alongside modern transformation and orchestration tooling (dbt, Airflow, or equivalents).
A genuine, proactive ownership instinct for data reliability, testing frameworks, and quality enforcement.
Ability to make complex data platform initiatives legible to non-technical stakeholders.

Nice To Haves

You have designed data models or an entire warehouse layer from scratch, rather than only maintaining someone else's legacy setup.
You have measurably improved the cost, performance, or reliability metrics of a production data platform.
Note: We use Snowflake, AWS, and dbt, but we care much more about your foundational systems engineering capabilities and ability to learn than any specific tool familiarity.

Responsibilities

Build the Data Foundation: Design and build scalable data pipelines that move, transform, and deliver high-quality data to AI, analytics, and product teams.
Data Warehousing: Develop and maintain our data warehouse footprint and custom data models that serve as the structural foundation for employee experience insights.
Platform Capabilities: Build shared data platform capabilities that make it faster, safer, and easier to deliver reliable data across the broader global engineering organization.
End-to-End Ownership: Take pipelines from initial ingestion through transformation, testing, monitoring, and rigorous data quality validation.
Cross-Functional Collaboration: Partner closely with the AI team, product engineering, and business analytics to get clean data into production in tight, iterative loops (idea → prototype → real-world use).
Engineering Excellence: Help define how this new regional team executes data engineering—including practices, standards, and cloud architecture. Share what you learn and uplift the engineers around you.