Business Intelligence Data Engineer

Details

• Collaborate with cross-functional teams to identify, collect, and validate data sources and analytics requirements
• Design, develop, implement and maintain data systems and ETL/ELT processes that support data processing, data integration, and data transformation.This includes the set up and management of Digital Science’s Fivetran instance, as well as developing and maintaining code base in cloud functions/GBQ pipelines for API calls to non-Fivetran supported data sources
• Develop and maintain data transformation and modelling processes using Dataform or dbt
• Implement and maintain best practices for data governance, data security, and data quality, alerting systems and CI/CD processes
• Play a central role in business system procurement and change management processes, to advise on the downstream impact for reporting and analytics of system modification, integration and implementation
• Stay current with industry trends and technologies to identify opportunities for innovation and improvement
• Experience as a Data Engineer or similar role working within a modern, cloud-based environment
• Strong SQL development skills, including experience designing modular, maintainable data models and transformations
• Hands-on experience with cloud data warehouses, preferably Google BigQuery, including performance optimisation, cost management and working with large datasets.
• Experience developing and maintaining ELT/ETL pipelines with tools such as Fivetran, Stitch, Airbyte or similar.
• Practical experience with SQL-based transformation frameworks such as Dataform or dbt, including managing pipelines as code and version control.
• Good understanding of data modelling principles (e.g. dimensional modelling) and best practices for data quality, lineage tracking and governance.
• Ability to design robust, testable, and scalable data products and processes, with attention to reliability, documentation and long-term maintenance.
• Understanding of DevOps practices (CI/CD, Git workflows, environment management).
• Excellent communication skills, enabling effective collaboration with technical and non-technical stakeholders
• Exposure to Google Cloud Platform (GCP) services beyond BigQuery, such as Cloud Functions, Cloud Storage, Pub/Sub or Cloud Run.
• Experience with data quality frameworks and automated monitoring, including alerting,anomaly detection, or testing frameworks.
• Familiarity with data visualisation and BI tools, such as Looker, Looker Studio, Tableau or Power BI.
• Knowledge of AI/machine learning concepts, and experience using tools such as Vertex AI, BigQuery ML or similar.