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Data Science Developer - Senior (ONSITE) JP210

@ Pathlion

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Toronto, Ontario

Temps plein
Postuler
SIGNAUX

En bref

Pathlion is hiring a senior data science developer for a full-time onsite role. The work covers designing and maintaining cloud-based data lakes, lakehouses and automated pipelines, migrating legacy Azure pipelines to Databricks, and supporting code reviews and knowledge transfer.

Responsabilités :

  • Design and maintain cloud data lake and lakehouse structures and automated pipelines
  • Migrate legacy Azure Synapse and Data Factory pipelines to Databricks with Delta Lake
  • Support product teams and business partners on cloud data and analytics products
  • Conduct code reviews and knowledge transfer to enforce development standards

Résumé des exigences :

  • Experience with cloud data platforms, preferably Microsoft Azure
  • Strong background building pipelines in Azure Data Factory and Databricks
  • Proficient in Python and SQL for data engineering
  • Familiar with medallion architecture, CI/CD and GitHub
  • Experience working on an Agile team

Outils et compétences

Azure Data FactoryDatabricksAzure Synapse AnalyticsAzure Data Lake StorageDelta LakeUnity CatalogGitHubParquet
Source et dates

Type de source non vérifié · manatal

Date publiée par l’employeur
2026-07-09
Première détection par notre source
2026-11-06
Mise à jour du dossier source
2026-11-06

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

Détails du poste

NiveauSenior
DomaineDéveloppement logiciel
Heures par semaine40 (estimé)
Milieu de travailBureau
PositionSurtout assis
Exigences physiquesphysique faible · ordinateur élevé
LanguesEnglish

Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.

Postuler
ChampValeur
Offres sur Jobsy82
Description

Responsibilities

- Participate in product teams to analyze systems requirements, architect, design, code and implement cloud-based data and analytics products that conform to standards

- Design, create, and maintain cloud-based data lake and lakehouse structures, automated data pipelines, analytics models

- Liaises with cluster IT colleagues to implement products, conduct reviews, resolve operational problems, and support business partners in effective use of cloud-based data and analytics products.

- Analyze complex technical issues, identify alternatives and recommend solutions.

- Support the migration of legacy data pipelines from Azure Synapse Analytics and Azure Data Factory (including stored procedures, views used by BI teams, and Parquet files in Azure Data Lake Storage (ADLS)) to modernized Databricks-based solutions leveraging Delta Lake and native orchestration capabilities

- Support the development of standards and a reusable framework that streamlines pipeline creation

- Participate in code reviews and prepare/conduct knowledge transfer to maintain code quality, promote team knowledge sharing, and enforce development standards across collaborative data projects.

General Skills

- Experience in multiple cloud-based data and analytics platforms and coding/programming/scripting tools to create, maintain, support and operate cloud-based data and analytics products, with a preference for Microsoft Azure

- Experience with designing, creating and maintaining cloud-based data lake and lakehouse structures, automated data pipelines, analytics models in real world implementations

- Strong background in building and orchestrating data pipelines using services like Azure Data Factory and Databricks

- Demonstrated ability to organize and manage data in a lakehouse following medallion architecture;

- Background with Databricks Unity Catalog for governance is a plus

- Proficient in using Python and SQL for data engineering and analytics development

- Familiar with CI/CD practices and tools for automating deployment of data solutions and managing code lifecycle

- Comfortable conducting and participating in peer code reviews in GitHub to ensure quality, consistency, and best practices

- Experience in assessing client information technology needs and objectives

- Experience in problem-solving to resolve complex, multi-component failures

- Experience in preparing knowledge transfer documentation and conducting knowledge transfer

- Experience working on an Agile team

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