Principal Data Engineer

This is a fully remote position, open to applicants in Canada.

📋 Description

• Direct the technical strategy for data flow, scalability, and decision-making processes at JobGet.

• Take ownership of the data architecture that connects job seekers with employers on the platform.

• Lead design evaluations for essential platform elements, assessing scalability, cost, reliability, and time-to-insight trade-offs.

• Assess and implement contemporary data stack tools, including Databricks, Snowflake, dbt, and Kafka.

• Manage observability, alerting, and operational readiness standards for tier-1 data pipelines.

• Design and create robust batch data pipelines utilizing Snowflake and dbt.

• Establish data modeling standards, covering schema design, dimensional modeling, and data contracts.

• Tackle complex challenges related to data integration, performance, and reliability through practical implementation.

• Architect and enhance streaming infrastructure using KSQL and Apache Flink.

• Collaborate with Platform Engineers and Data Analysts to design feature stores, training data pipelines, and datasets ready for inference.

• Ensure data products are well-structured, documented, and optimized for self-service analytics.

• Assess and integrate innovative AI-powered tools.

• Define and uphold data governance standards concerning quality, lineage, access control, and cataloging.

• Implement data validation frameworks, SLA monitoring, and anomaly detection mechanisms.

• Design privacy-centric data systems in accordance with CCPA, GDPR, and relevant regulations.

• Mentor and develop data engineers through code reviews and design guidance.

• Collaborate with Software Engineering, Product, and Analytics teams to address data requirements.

• Promote the adoption of data platform standards and architectural patterns across various teams.

• Identify and resolve bottlenecks, architectural flaws, and performance challenges impacting system stability.

⛳️ Requirements

• Over 10 years of experience in data engineering.

• A minimum of 3–5 years working at a principal or staff IC level within a startup environment.

• Proven production-grade experience with Snowflake and dbt (mandatory).

• Strong preference for experience with real-time streaming technologies such as KSQL or Apache Flink.

• Practical AWS experience at scale, encompassing cloud data architecture, cost governance, and infrastructure-as-code.

• Experience with Terraform is beneficial.

• Familiarity with DataOps practices, including CI/CD for data pipelines, automated testing frameworks, and deployment processes.

• Significant, demonstrated use of AI in daily engineering tasks is required.

• Expert-level skills in Python and SQL.

• Strong understanding of distributed systems, data pipeline design, and performance optimization.

• Extensive background in data modeling, including dimensional modeling, schema design, and data contracts.

• Experience in constructing data infrastructure that supports ML/AI systems, including feature pipelines, training data management, and inference-layer data serving.

• Proven track record of establishing technical standards across teams through RFCs, architecture reviews, or platform migrations.

• Deep understanding of data governance principles, including data lineage, cataloging, access control, and quality monitoring.

• Familiarity with real-time and near-real-time data architectures, including streaming, event-driven design, and CDC.

• Exceptional communication and stakeholder management abilities.

• A builder’s mentality combined with operational discipline.

🏝️ Benefits

• Mission-driven organization.

• Flexible time off policies.

• Remote-first work environment.

• Flexible working hours.

• Comprehensive medical and dental plans.

• Parental leave benefits.

• Employee stock options.