Technical standards and convergence

• Define and maintain the data engineering standards that apply across the portfolio, covering ingestion patterns, layered data modelling, DataOps practices, data lineage, data quality, workflow observability, and metadata documentation. Standards must be practical and documented well enough to be followed by engineers across multiple organisations who are not part of the central DFTO Data team.
• Where implementations exist across the TOC community that are variants of the same underlying pattern, lead convergence toward a shared canonical version, managing the transition from locally held to centrally maintained in a way that is practical and agreed with the originating organisation.
• Ensure shared artefacts are catalogued, documented, and published to a standard that makes them genuinely discoverable and reusable by the wider community, not merely accessible in principle.
• Feed recurring delivery friction (e.g., gaps in standards, schema conflicts, supplier data access issues, governance bottlenecks) back into the appropriate structural channels for escalation and resolution.

Hands-on delivery and technical quality

• Remain a hands-on technical contributor on the most complex or architecturally critical initiatives, both to maintain credibility across the community and to set the practical standard for how the engineering team works.
• Provide technical oversight and quality assurance across the team's engineering outputs, reviewing approaches, data models, and implementation choices with the intent of raising capability across the team and the wider community.
• Support and mentor the Analytics Engineers within the central DFTO Data team, with particular attention to the cross-organisational and portfolio-level dimensions of the work that are unlikely to have featured in their previous roles.

Community and standards leadership.

• Act as the senior technical interface for initiative leads across the portfolio, representing the DFTO Data function with authority and maintaining productive working relationships with colleagues who are simultaneously portfolio collaborators and senior figures in their own organisations.
• Provide hands-on technical leadership to per-initiative working pods drawn from across the federated community (which could include data engineers and analysts from TOCs, NR, and RDG), maintaining consistent engineering standards and approach across teams with different organisational homes and tooling defaults.
• Contribute to shared data standards work across the wider cross-industry community, bringing data engineering grounding into standards discussions with counterparts in NR, RDG, and the TOC community.
• Help the broader community of data professionals working on portfolio initiatives understand and apply engineering and data standards in practice, through documentation, direct engagement, and leadership by example.

Knowledge, Skills, Experience & Technical Qualifications

• Demonstrated experience delivering data products in complex, multi-stakeholder environments, with a track record as a hands-on data or analytics engineer across the full stack from ingestion through to analytics-ready outputs.
• Proven ability to exercise technical authority across a hybrid team environment of direct reports and senior peers from partner organisations, earning influence through the quality of engineering judgement rather than positional authority.
• Strong SQL and proficiency in at least one analytics programming language, with Python strongly preferred. Ability to review, critique, and improve others' code as well as write it.
• Deep familiarity with layered data modelling approaches and transformation frameworks such as dbt, including the ability to define and enforce data modelling standards across a team.
• Experience designing and maintaining data ingestion and transformation pipelines across cloud-native environments, with a clear instinct for reusable, configuration-driven patterns over bespoke implementations.
• Comfort working across multi-cloud platform environments including AWS and Microsoft Azure/Fabric, with understanding of data platform architecture across storage, compute, and serving layers.
• DataOps disciplines at a leadership level: defining and enforcing CI/CD practices, environment lifecycle management, data quality frameworks, and documentation standards across a team.
• Experience managing the engineering interface with non-engineering stakeholders – translating product or domain requirements into engineering constraints, and holding a clear engineering position under pressure from people who are technically senior or organisationally influential.
• Ability to lead convergence of locally developed data assets toward shared canonical standards, including managing the organisational and technical dimensions of that transition.
• Clear written and verbal communication, including the ability to document technical standards to a level that data engineers outside the central team can follow independently.

Desirable

• Experience and delivery capability is more important than formal qualifications. We welcome candidates from non-traditional backgrounds who can demonstrate strong engineering judgement and a track record of delivery in complex environments.
• A degree in a STEM, quantitative, or related field may be beneficial but is not required.
• Experience in a technical lead or principal engineer role within a data product or analytics engineering function.
• Familiarity with data catalogue, metadata management, and data lineage tooling at a portfolio scale.
• Experience contributing to or leading cross-organisational data standards work, including schema harmonisation, reference data management, or interoperability frameworks.
• Familiarity with production machine learning and AI ops patterns, sufficient to assess whether data products are structured appropriately to support downstream ML use cases.
• Experience working in regulated or safety-adjacent industries where data quality and auditability standards are non-negotiable.
• Familiarity with railway industry data sources such as TRUST, Darwin, LENNON, timetables, or train unit diagrams is desirable but not expected.

Organisational Context

The postholder will be the senior technical role within a new central Data function at DFTO, working alongside an Analytics Engineer and under the strategic direction of the Group Head of Data, with support from the DFTO Architecture function. The wider working community spans data professionals across publicly owned TOCs, NR, and RDG. The postholder is expected to be a visible and credible presence within that community from day one – not only delivering within the central team but actively shaping how data engineering is practised across the ecosystem.

Vacancy Details

• Duration: Permanent
• Location: London Waterloo
• Salary: £90,000 - £104,500
• Closing date: 12th June 2026

DFTO Benefits

• Annual Leave: Starting at 25 days and rising to an additional day per year of service completed within the first 5 completed years up to a maximum of 5 additional (30 days)
• DC Pension Scheme: 10% Employer contribution, 5% Employee contribution
• Opportunities to learn and network across the wider industry

Additional Information

Disclaimer: Candidates applying for this position on a