GCP Senior Data Engineer Grade
Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Oct 16, 2026. Querentia scores C on the Alion truth index.
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.
Skills
GCP, Data Engineering