Data Engineer

• Own and optimise our platform architecture (improve reliability, scalability, maintainability, etc) • Build new data sources and data pipelines that deliver key data and insights to the business (this will include integration with third party systems and ingestion through APIs) • Scope and deliver new data engineering projects in collaboration with business stakeholders • Develop and deploy ML infrastructure to help build out our ever-growing AI requirements and use cases • Collaborate closely with our dev teams to build seamless integrations between our back-end databases and our data platform • Shape the direction of our growing team and coach team members on best practices

• First and foremost, a passion for decarbonisation • Ability to work with ambiguity and own problems end-to-end • A passion for writing high quality code and building lean processes • Experience with distributed data processing • Experience with monitoring, testing, and data quality • Experience building robust pipelines from diverse sources e.g. parquet, SQL & no-SQL databases, API endpoints • It would be helpful to have experience/expertise/knowledge in the following: • Python • AWS • Kubernetes • Spark & distributed computing • dbt • Terraform • Data Warehousing (we use Databricks but experience with other platforms is fine) • MLOps implementation (desirable but not required) • Airflow (or other orchestration tooling) • Governance