Data Engineer

The Role The Fastmarkets Data Platform team sits within the Technology function and is responsible for developing and delivering the company’s data strategy through data engineering, insights, analytics, and business intelligence. The team has built the organisation’s entire modern data platform from scratch and continues to evolve it to support Fastmarkets’ growth and AI ambitions.  Reporting directly into the Head of Data Architecture, Engineering & AI, we are looking for the right candidate to model and build our data pipelines and transformations. This is a fantastic opportunity to join us and contribute to a high-performing workstream delivering transformational change across both BI and our monetizable data assets. You will work with modern technologies and frameworks – with input to help deliver and adapt processes that enable a true “DataOps” environment and development experience.  The Data Engineer will ensure that data is cleansed, tested, and delivered in accordance with Fastmarkets and industry best practice. In addition, the Data Engineer will enable monitoring via the observability platform and ensure that notifications and alerts are appropriately configured.  We actively leverage AI tools across the data engineering workflow, including Anthropic Claude for documentation, code generation, and code review, as well as Snowflake Cortex AI capabilities for in-platform intelligence. The successful candidate will be expected to embrace AI-assisted development as a core part of how we work.  The role requires a high standard of expertise in SQL and Python as well as specialist knowledge of modern approaches to delivering data projects using a code-first approach.  Principal Accountabilities Design, develop, and maintain scalable and efficient data pipelines from a wide variety of sources  Use dbt (Data Build Tool) to transform data through the various layers (Cleansed, Conformed, Presentation etc)  Ability to write custom connectors (Python) and leverage out-of-the-box data-loading tools  Operationalise enterprise data model by curating appropriate data models to service reporting, analytics, and data science use cases  Embed real-time, automated data quality checks, validations, exception handling, and alerting across all data pipelines  Work within Dagster as the primary orchestration and observability platform for all data pipelines  Manage CI/CD workflows using GitHub, GitHub Actions, and Kubernetes  Use zero-copy cloning and containerised on-demand development environments  Implement and review RBAC policies within Snowflake and related platforms  Embrace DataOps and a code-first approach to all data engineering work  Identify and promote best practices in data engineering; recommend improvements to existing processes and systems  Leverage AI-assisted development tools (Anthropic Claude,…