GCP Data ETL Engineering GCB

Verita HR is hiring for a prestigious position at one of the world's largest banks, focusing on stable, long-term projects involving data engineering with Google Cloud Platform technologies.

• Proven (3+ years) hands-on experience in SQL querying and optimization of complex queries/transformations in BigQuery, focusing on cost- and time-effective coding and concurrency/data integrity.
• Proven (3+ years) hands-on experience in SQL data transformation/ETL/ELT pipeline development, testing, and implementation, ideally using GCP Datafusion.
• Experience with Data Vault modeling.
• Hands-on experience with Cloud Composer/Airflow, Cloud Run, Pub/Sub.
• Hands-on development experience with Python and Terraform.
• Proficiency in Git for version control and collaboration.
• Proficiency with CI/CD process design, creation, and maintenance using DevOps tools like Ansible and Jenkins, ideally for cloud-based applications on GCP.
• Experience working in a DataOps model.
• Experience working in Agile environments and using associated toolsets.
• Strong problem-solving and analytical skills.
• Enthusiastic willingness to rapidly and independently learn and develop technical and soft skills.
• Strong organizational and multi-tasking skills.
• Good team player embracing teamwork and mutual support.

• Design, build, test, and deploy Google Cloud data models and transformations in a BigQuery environment (e.g., SQL, stored procedures, indexes, clusters, partitions, triggers).
• Create and manage ETL/ELT data pipelines to model raw/unstructured data into Data Vault universal model.
• Enrich, transform, and optimize raw data for end consumer usage.
• Review, refine, interpret, and implement business and technical requirements.
• Deliver non-functional requirements, IT standards, and developer/support tools ensuring secure, compliant, scalable, reliable, and cost-effective applications.
• Monitor data pipelines for failures or performance issues and implement fixes or improvements.
• Optimize ETL/ELT processes for performance and scalability to efficiently handle large volumes of data.