Principal Data Engineer

JobGet•Vancouver, BC

3d•CA$160,000 - CA$190,000•Remote

About The Position

As our Principal Data Engineer, you will help lead the technical direction for how data flows, scales, and powers every decision at JobGet. You’ll own the data architecture behind our platform that connects hundreds of millions of job seekers with employers across dozens of industries; and you’ll do it at the intersection of high-throughput pipelines, real-time matching signals, and AI-driven product experiences. Data Platform Architecture & Strategy 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. Data Modeling & Pipeline Engineering 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, and 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. AI & Analytics Enablement 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. Data Governance, Quality & Security 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. Hands-On Execution & Technical Excellence 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.

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.
Familiarity with real-time and near-real-time data architectures (streaming, event-driven design, CDC), especially in marketplace or two-sided platform contexts.
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.

Responsibilities

Help lead the technical direction for how data flows, scales, and powers every decision at JobGet.
Own the data architecture behind the platform that connects hundreds of millions of job seekers with employers across dozens of industries.
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.
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, and 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.
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.