Postuler

Publié il y a 5 mois

Data Engineer

@ PartsAvatar.ca

Montréal, Québec

Temps plein
Postuler
SIGNAUX

Résumé des exigences :

  • Degree required in Computer Science, Software Engineering, or a related quantitative discipline.
  • Technical Skills
  • Expertise in Python development and comprehensive experience with data-handling packages (e.g., Pandas/Polars, NumPy).
  • Fluency in advanced SQL for complex data query logic.
  • Familiarity with cloud platforms (AWS, Azure, or Google Cloud) and Big Data frameworks.
  • Robust familiarity with MLOps workflows and automation.
Source et dates

Type de source non vérifié · apify-linkedin

Date publiée par l’employeur
2026-06-14
Première détection par notre source
2026-11-11
Mise à jour du dossier source
2026-11-11

Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.

PartsAvatar.ca

ChampValeur
Offres sur Jobsy3
Description

Company: Parts Avatar

Location: Downtown Montreal, Quebec (Hybrid: 3 days in office, 2 days remote)

Employment Type: Full-time

About Parts Avatar

Parts Avatar approaches the entire automotive repair and aftermarket industry through a rigorous scientific and technological lens, positioning our e-commerce platform as just one core solution alongside a broader ecosystem of engineering innovations. We operate fundamentally as a technology enterprise, powering our mission through completely distinct, highly specialized Software Engineering and Data Science teams. These separate units dedicate themselves to mastering complex industrial, supply chain, and operational challenges, collaborating to pioneer smart, data-driven solutions that optimize workflows and completely redefine the automotive landscape. Join our growing team in a dynamic, deeply analytical, and innovative environment!

Job Summary

We are seeking a Python-focused Data Engineer to bridge the gap between data infrastructure and data science. In this role, you will lead the standardization and optimization of our data pipelines, specifically built for data science and machine learning projects. You will introduce modern technology stacks and MLOps practices to ensure our models are robust, scalable, and efficiently deployed into production environments.

Key Responsibilities

Work on the company's data pipeline, contributing heavily to its standardization, development, and optimization.

Build clean Python abstractions and automated pipelines to optimize data ingestion and preprocessing for analytics and machine learning.

Enforce rigorous data quality initiatives, testing, and validations to ensure data integrity across upstream and downstream systems.

Collaborate with data scientists to architect, package, configure, and deploy machine learning models using MLOps frameworks.

Evaluate and integrate new technologies to leverage and scale our current data science engineering stack.

Generate actionable insights for business and infrastructure improvements.

Qualifications

Education

Degree required in Computer Science, Software Engineering, or a related quantitative discipline.

Technical Skills

Expertise in Python development and comprehensive experience with data-handling packages (e.g., Pandas/Polars, NumPy).

Fluency in advanced SQL for complex data query logic.

Familiarity with cloud platforms (AWS, Azure, or Google Cloud) and Big Data frameworks.

Robust familiarity with MLOps workflows and automation.

Proficiency with Git version control and the Atlassian suite (Jira, Confluence).

Soft Skills

Curiosity and a strong desire to learn and grow.

Ability to take initiative, work independently, and collaborate effectively within a cross-functional squad.

Excellent multitasking capabilities.

Work Authorization

Must be eligible to work in Canada.

Language Requirements

Proficiency in English (spoken and written) is required, as English is the primary working language across teams in Ontario and Quebec. French is considered an asset.

Benefits

Competitive salary.

Performance bonus.

Comprehensive insurance plan.

Hybrid work schedule (3 days in office, 2 days work from home).

Opportunity to work with a dynamic and growing e-commerce leader.

Collaborative and supportive team environment.

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