Publié il y a 25 jours
GCP Senior Data Engineer Grade
@ Querentia Inc.
Télétravail au Canada
En bref
Querentia Inc. is hiring a GCP Senior Data Engineer on a contract basis, fully remote in Canada. The role leads a team of 10 data engineers building scalable pipelines and cloud data infrastructure on Google Cloud Platform, with code reviews, engineering standards and support for analytics and machine learning.
Responsabilités :
- Lead a team of 10 data engineers
- Build and optimize ETL/ELT pipelines on GCP
- Architect BigQuery, Cloud Storage and Data Fusion infrastructure
- Implement CI/CD, infrastructure as code and monitoring
- Optimize performance and GCP costs
Résumé des exigences :
- Proven leadership of onshore and offshore data engineering teams
- Proficiency in SQL, Python, Java/Scala
- Hands-on GCP experience with BigQuery, Dataflow, Dataproc, Pub/Sub, Composer, Terraform
- Familiarity with CI/CD and Git
- Experience building and optimizing data pipelines
Outils et compétences
Source et dates
Type de source non vérifié · jsonld
- Date publiée par l’employeur
- 2026-10-15
- Première détection par notre source
- 2026-10-26
- Mise à jour du dossier source
- 2026-11-05
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Développement logiciel |
| Gestion d’équipe | Oui |
| Déplacements | avion : aucun · route : aucun |
| Milieu de travail | Domicile |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Querentia Inc.
| Champ | Valeur |
|---|---|
| Offres sur Jobsy | 331 |
GCP Senior Data Engineer Grade
Work Location: Canada (Remote)
Job Summary
The GCP Senior Data Engineer will lead a team of 10 data engineers in designing, developing, and optimizing scalable data pipelines and cloud-based data infrastructure on Google Cloud Platform (GCP). This is a leadership-focused role requiring experience managing onshore and offshore teams, establishing engineering standards, and delivering reliable data solutions to support analytics and machine learning use cases.
Key Responsibilities
•
Pipeline Development: Build and optimize ETL/ELT pipelines using Dataflow (Apache Beam), Dataproc (Spark), Pub/Sub, and Cloud Functions.
•
Data Infrastructure: Architect and manage BigQuery, Cloud Storage, and Data Fusion for efficient data ingestion, transformation, and storage.
•
Team Leadership: Lead a team of 10 engineers, conduct code reviews, and establish data engineering standards.
•
DevOps & Monitoring: Implement CI/CD practices, infrastructure-as-code, and monitoring using Cloud Monitoring and Cloud Logging.
•
Performance & Cost Optimization: Improve pipeline performance and query efficiency while managing GCP costs through techniques such as slot reservations and partitioning.
•
Collaboration: Work with data architects, data scientists, and analysts to deliver self-service data platforms and support advanced analytics and machine learning use cases.
•
Data Quality & Governance: Implement data validation, lineage, and metadata management using tools such as Data Catalog and dbt.
Required Qualifications
•
Leadership: Proven experience leading data engineering teams across onshore and offshore locations. Candidates must have leadership experience; this is not an individual contributor-only role.
•
Programming & Distributed Systems: Proficiency in SQL, Python, Java/Scala, and distributed processing frameworks such as Spark and Beam.
•
GCP Expertise: Hands-on experience with BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer (Airflow), and Terraform.
•
DevOps: Familiarity with CI/CD, Git, and infrastructure-as-code practices.
•
Data Engineering: Experience building and optimizing data pipelines, managing cloud data infrastructure, and implementing data quality and monitoring practices.
Nice to Have
•
GCP certifications, such as Google Cloud Professional Data Engineer.
•
Experience with real-time data processing.
•
Familiarity with Vertex AI and machine learning workflows.
•
Experience working in multi-cloud environments.
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