Description:
The Analytics Engineer is a core member of the DFTO Data function, responsible for the hands-on design and delivery of data products across the Common Data Service portfolio. The portfolio is DFTO's cross-industry data capability: ingesting, standardising, and publishing shared data products for use across the GB rail ecosystem, in preparation for the establishment of Great British Railways.
This is a “full-stack” data delivery role, combining data engineering, analytical modelling, and cross-organisational working. The postholder takes end-to-end ownership of their data products – from problem definition and ingestion pipeline design through to the analytical and presentation layer that makes those products discoverable, interpretable, and usable by functional teams across the GB railway ecosystem. The environment is genuinely multi-organisational from day one: the postholder will work with counterparts across train operating companies (TOCs), Rail Delivery Group (RDG), and Network Rail (NR) as delivery peers, earning credibility through the quality and consistency of their engineering output.
Key Responsibilities
Cross-industry data product delivery
- Engage with functional teams across DFTO, TOCs, NR, and RDG to translate domain expertise and analytical need into well-scoped, deliverable data product designs, working with the Principal Analytics Engineer to maintain engineering coherence across concurrent initiatives.
- Build and deliver shared data products that are catalogued, governed, discoverable, and engineered to a standard that remains maintainable beyond the initial build effort.
- Design data models that reflect real-world railway concepts (e.g., passenger experience, train service delivery, rolling stock) and which support consistent, reusable analytics across the industry.
- Develop the analytical and presentation layer on top of shared data products (e.g., summaries, visualisations, and contextual documentation) so that the output is usable by functional teams on a self-serve basis.
- Ensure data products are structured, documented, and published in a way that supports machine learning, AI, and workflow automation use cases – including clear schema definitions, quality metadata, and access patterns that can be consumed programmatically.
- Document data processes, schemas, and transformation logic to a standard that allows engineers and analysts outside the central team to understand, validate, and build upon the outputs.
Data integration and modelling
- Build and maintain data ingestion pipelines across a multi-cloud platform environment, drawing in feeds from operational, performance, commercial, and third-party source systems across the railway ecosystem.
- Design and implement layered data transformations from raw ingestion through to cleansed, analytics-ready models, maintaining adherence to agreed architectural patterns.
- Develop reusable, generalisable ingestion and transformation patterns rather than bespoke per-source implementations, so that adding a new data source to the portfolio is a configuration exercise rather than a new engineering project.
- Contribute to shared data standards across the ecosystem – working with counterparts in TOCs, NR, and RDG to align schemas, definitions, and data quality expectations so that data products built at any level can interoperate with and build upon each other.
- Support cross-organisational data sharing at a technical level: governed access patterns, data catalogue publication, metadata standards, and the API or query surface through which data consumers interact with shared products.
Data engineering standards and governance practices
- Apply DataOps disciplines consistently across all delivery: CI/CD pipelines, Git version control, environment lifecycle management (development, test, production separation), role-based access controls, and peer review processes.
- Contribute to the definition and continuous improvement of shared data engineering standards across the cross-industry delivery community, including counterparts in TOCs, NR, and RDG.
- Maintain data quality and data catalogue entries across assigned products, including lineage documentation, quality metrics, and lifecycle status.
- Identify and surface delivery-level friction (e.g., supplier data access gaps, schema conflicts, governance bottlenecks) as structured inputs to the data standards and governance function for escalation and resolution.