Description
Cloud-native data-engineering role designing scalable pipelines and data platforms on Google Cloud, with exposure to AI model operationalisation and formal testing.
Key responsibilities
- Design and maintain end-to-end ELT/ETL pipelines for high-volume, multi-source data.
- Build data models, warehouses and lakes on GCP using BigQuery, Cloud Storage, Dataflow, Dataproc and Pub/Sub.
- Develop Python transformations, orchestration, automation and data-quality checks.
- Integrate and operationalise AI prediction models within cloud data pipelines.
- Plan and execute UAT and QA activities, data validation and release-readiness controls
- Implement monitoring, logging, alerting, governance, security, privacy and compliance controls.
- Maintain technical documentation, data dictionaries, runbooks and test plans.
Requirements
- Strong Python development, including design patterns, testing, packaging and performance considerations.
- Hands-on GCP experience with BigQuery, Cloud Storage, Dataflow / Beam, Dataproc, Pub/Sub and Cloud Composer / Airflow.
- SQL, data modelling, schema design and production data-pipeline experience.
- Understanding of AI prediction models, data preparation, feature stores and MLOps principles.
- Git, orchestration and CI/CD for data pipelines.
- French at C1 minimum, written and spoken, for active stakeholder engagement.
- Professional English and strong communication, documentation and autonomous delivery skills.
Preferred / differentiating requirements
- UAT, QA, test planning, data validation and test automation.
- Spark / Databricks and Control-M or comparable enterprise scheduling.
- GCP Professional Data Engineer certification or equivalent practical expertise.
- Vertex AI, streaming data with Kafka or Pub/Sub, and real-time analytics.
- Looker, Tableau or Power BI.
- Experience in regulated industries and data-governance frameworks.
Benefits
The benefits will be determined once the contractual terms have been established.
