Data Engineer (Early Career) (Canada)

Jobright is your personal AI job search agent transforming the job search process. They are seeking an early-career Data Engineer to design and build data pipelines, ensuring data quality and collaborating with analytics and product teams.

Why Join Us

• • Build real, production AI agents used by real users
• • High ownership and impact
• • Work at the intersection of AI, agents, and product
• • Shape how people experience AI-driven job search

Responsibilities

• • Design and build reliable data pipelines that move data from diverse sources into centralized warehouses and lakes for downstream consumption
• • Own the full lifecycle of ETL/ELT processes, from schema design and ingestion to transformation, testing, and deployment
• • Ensure data quality and consistency by implementing validation checks, anomaly detection, and automated alerting across critical pipelines
• • Partner with analytics, data science, and product teams to understand their data needs and deliver clean, well-documented datasets
• • Optimize query performance, storage costs, and pipeline reliability as data volumes and complexity grow

Qualification

Required

• • Recent graduate or early-career professional (0–2 years of experience) with a degree in Computer Science, Data Engineering, or a related technical field
• • Strong proficiency in Python and SQL, with hands-on experience writing and debugging data transformation logic
• • Practical understanding of data modeling, warehouse design patterns, and the trade-offs between batch and streaming architectures
• • Solid software engineering foundations, including comfort with APIs, version control (Git), and writing testable, production-ready code

Preferred

• • Previous internship or project experience building end-to-end data pipelines or managing datasets at non-trivial scale
• • Familiarity with cloud data platforms such as Snowflake, BigQuery, Redshift, or Databricks
• • Hands-on exposure to orchestration tools like Airflow, dbt, or Prefect, and streaming technologies like Kafka or Spark Streaming
• • Curiosity about how data powers product decisions, and a willingness to dig into messy, ambiguous data problems