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Publié il y a 20 jours

Lead Data Engineer

@ Flow Global Technology

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Vancouver, Colombie-Britannique

Contrat
Postuler
SIGNAUX

En bref

Flow Global Technology is hiring a Lead Data Engineer on a contract basis to design and build scalable batch and streaming data pipelines and data platforms. The role covers data services and APIs, data modeling, data quality and observability, and mentoring data engineers. Candidates need strong Java, Spark, Kafka and Spring Boot experience.

Responsabilités :

  • Design and develop batch and streaming data pipelines with Java, Spark and Kafka
  • Architect data platforms across ingestion, storage and serving
  • Build data services and APIs with Spring Boot
  • Mentor and coach data engineers

Résumé des exigences :

  • Experience leading data platform and pipeline design
  • Strong Java, Spark and Kafka skills
  • Knowledge of data governance and PIPEDA compliance
  • Agile delivery experience

Outils et compétences

JavaSpring Boot
Source et dates

Type de source non vérifié · apify-linkedin

Date publiée par l’employeur
2026-10-21
Première détection par notre source
2026-11-10
Mise à jour du dossier source
2026-11-10

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

Détails du poste

NiveauChef d’équipe
DomaineDéveloppement logiciel
Milieu de travailBureau
PositionSurtout assis
Exigences physiquesphysique faible · ordinateur élevé

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

Postuler

Flow Global Technology

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ChampValeur
Offres sur Jobsy1
Description

Key Responsibilities

  • Lead the design and development of scalable batch and streaming data pipelines using Java, Spark, and Kafka
  • Architect data platforms covering ingestion, transformation, storage, and serving layers
  • Build and maintain data services and APIs using Spring Boot
  • Define data models, partitioning strategies, and schema management for data lake and warehouse environments
  • Own code quality through code reviews, design reviews, and engineering best practices
  • Mentor and coach data engineers, and support their technical growth
  • Optimize pipeline performance, cost, and JVM/Spark tuning
  • Implement data quality checks, monitoring, alerting, and observability
  • Drive CI/CD, automated testing, and infrastructure-as-code practices
  • Lead root-cause analysis for production data incidents
  • Ensure data security, governance, and privacy compliance (including PIPEDA requirements)
  • Contribute to estimation, sprint planning, and delivery in an Agile environment

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Lieux et modes de travail

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