Principal Data Engineer - Canada

JobGet

7d•Remote

About The Position

JobGet is the #1 app focused on everyday workers, redefining the future of hiring. Founded in 2019, JobGet began as the only mobile-first hiring platform for everyday workers. Since then, JobGet has grown by joining forces with Snagajob, the largest hourly job board in the U.S., followed by Seasoned, the leading platform for restaurant hiring. Each acquisition has brought JobGet closer to the frontline, making the platform faster, smarter, and more effective. While most platforms chase volume, JobGet is laying the foundation for a new kind of hiring infrastructure focused on precision, speed, and real outcomes. Our AI-powered engine learns, adapts, and improves continuously. Employers don’t just get candidates, they get hires. Workers don’t just search, they get matched. The result is a more efficient hiring process with less friction and better results. We serve a diverse range of industries, from food service and customer support to healthcare, logistics, and manufacturing. With access to over 200 million job seekers, our platform leverages cutting-edge AI to match qualified, role-ready candidates, automating key parts of the hiring process for faster decisions and better outcomes. By combining scale with precision, we’re transforming how businesses hire and how people find meaningful work. Our mission is simple: to help everyday workers thrive by providing accessible, frictionless hiring solutions that deliver real results. Join us as we continue to build the future of work for the millions of people who power our world every day. Learn more about our culture here.

Requirements

10+ years of data engineering experience, with at least 3–5 years operating at a principal or staff IC level in a start-up environment.
Production-grade experience with Snowflake and dbt (required); you’ve designed, owned, and scaled these in a real product environment, not just used them.
Hands-on AWS experience at scale, including cloud data architecture, cost governance, and infrastructure-as-code.
DataOps experience: familiarity with CI/CD for data pipelines, automated testing frameworks, and deployment practices that treat data infrastructure like production software.
Significant, demonstrated AI usage in your day-to-day engineering work is required. You actively use AI tools (e.g. Claude, Copilot, Cursor, or similar) to move faster, reason through architecture, generate and review code, and solve novel problems.
Expert-level proficiency in Python and SQL, with strong command of distributed systems, data pipeline design, and performance optimization.
Strong data modeling background: dimensional modeling, schema design, data contracts, and the ability to build structures that outlast the team member who built them.
Experience building data infrastructure that directly supports ML/AI systems, including feature pipelines, training data management, and inference-layer data serving.
Demonstrated track record of setting technical standards across teams, not just within your own scope: through RFCs, architecture reviews, or platform migrations.
Strong grasp of data governance fundamentals (data lineage, cataloging, access control, and quality monitoring) with practical experience implementing them in production.
Exceptional communication and stakeholder management skills; you can translate complex data infrastructure trade-offs into plain language for product and business audiences.
You have a builder’s mentality and an operator’s discipline. You move fast, ship with confidence, and hold yourself to a high bar even when no one is watching.

Nice To Haves

Experience with real-time streaming technologies such as KSQL or Apache Flink is strongly preferred; you’ve built or operated streaming pipelines in production.
Terraform experience is a nice-to-have.
Familiarity with real-time and near-real-time data architectures (streaming, event-driven design, CDC), especially in marketplace or two-sided platform contexts.

Responsibilities

Drive Architectural Decisions: Lead design reviews for critical platform components, evaluating trade-offs across scalability, cost, reliability, and time-to-insight.
Champion Modern Data Stack Adoption: Evaluate and introduce best-in-class tooling (Databricks, Snowflake, dbt, Kafka, etc.) aligned with engineering principles and business needs.
Ensure Platform Reliability: Own the observability, alerting, and operational readiness standards for tier-1 pipelines, including runbooks, failover strategies, and reprocessing protocols.
Autonomy & Decision Making: Demonstrate high resourcefulness; you are empowered to make significant technical decisions and drive projects end-to-end with limited oversight.
Build Production-Grade Pipelines: Design and implement robust batch data pipelines using Snowflake and dbt as the core of JobGet’s transformation layer, ingesting and serving data across product telemetry, marketplace signals, employer activity, and candidate behavior.
Lead Data Modeling Decisions: Establish data modeling standards (schema design, dimensional modeling, data contracts) that serve analytics, ML feature engineering, and product instrumentation.
Solve the Hardest Problems: Personally take on the most complex data integration, performance, and reliability challenges that require principal-level judgment and hands-on implementation.
Support Real-Time & Near-Real-Time Needs: Architect and evolve streaming data infrastructure using technologies such as KSQL and Apache Flink to power real-time matching, recommendation, and product decisioning systems.
Enable Machine Learning at Scale: Partner closely with Platform Engineers and Data Analysts to design feature stores, training data pipelines, and inference-ready datasets that power JobGet’s AI matching engine.
Accelerate Analytics Delivery: Work with Data Analytics stakeholders to ensure data products are well-modeled, documented, and performant for self-serve consumption.
Evaluate and integrate emerging AI-powered tools to enhance operational efficiency and developer productivity.
Establish Data Governance Standards: Define and enforce policies for data quality, lineage, access control, and cataloging that scale across a growing and multi-product organization.
Protect Data Integrity: Implement data validation frameworks, SLA monitoring, and anomaly detection to maintain trustworthy data across all consumers.
Ensure Compliance & Privacy: Design data systems with privacy-by-default principles, ensuring alignment with applicable regulations (CCPA, GDPR, etc.) across candidate and employer data.
Raise the Technical Bar: Mentor and level up engineers across the data team through code reviews and design guidance.
Partner Across Engineering & Product: Collaborate with Software Engineering, Product, and Analytics to ensure data requirements are deeply understood at inception.
Influence Without Authority: Drive adoption of data platform standards and architectural patterns across teams through clear communication, documentation, and trust-building.
Proactive Problem Solving: Personally identify and execute fixes for deep-seated bottlenecks, architectural bugs, and performance issues that impact system stability.