Senior Data Engineer

Design, build, test and deploy Google Cloud data models and transformations in BigQuery environment (e.g. SQL, stored procedures, indexes, clusters, partitions, triggers, etc.).
Create and manage ETl/ELT data pipelines to model raw/unstructured data into Data Vault universal model, enriching, transforming and optimizing data into for end-user consumption.
Review and refine, interpret, and implement business and technical requirements.
Ensure applications meet non-functional requirements, IT standards, and security, compliance, scalability, reliability, and cost-efficiency needs.
Monitor and optimize data pipelines for failure, performance issues, and scalability, implementing fixes or improvements as needed.
Integrate data from multiple sources, ensuring consistency and accuracy.
Manage code artefacts and CI/CD using tools like Git, Jenkins, Google Secrets Manager, etc.
Address defects, enhance development, and transition knowledge, code, and support responsibilities to the support team.

Proven (3+ years) hands-on experience in SQL querying and optimization of complex transformation in BigQuery, focusing on cost-efficiency, concurrency, and data integrity.
Proven (3+ years) hands-on experience in SQL Data Transformation, ETL/ELT pipeline development, testing, and implementation, ideally in GCP Datafusion.
Experience in Data Vault modelling and implementation.
Hands on experience in Cloud Composer/Airflow, Cloud Run, and Pub/Sub.
Development experience in Python, Terraform, including proficiency in Git for version control and CI/CD pipeline creation and maintenance using DevOps tools like Ansible/Jenkins for could-based applications (ideally GCP).
Experience working in DataOps model and Agile environment with corresponding toolsets.
Strong problem-solving, analytical, and organisational skills with the ability to adapt, learn, and develop technical and soft skills rapidly and independently.
Collaborative team player who embraces teamwork and mutual support.

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