Principal Analytics Engineer

Job Ref: DOH1220Branch: DFT OperatorLocation: Waterloo, LondonSalary/Benefits: £90,000\-£104,500 (Permanent)Contract type: PermanentHours: Full TimeHours per week: 35Posted date: 29/05/2026Closing date: 14/06/2026 About DFT Operator

Join Our Team at DFTO

DFTO is the government’s public sector rail owning group. Its purpose is to bring all currently privately\-owned train operators into public ownership in advance of the creation of Great British Railways in 2027 \- and deliver improvements in the here and now by unifying and integrating train operations under common public ownership.

DFTO has over 30,000 employees, runs over 8,500 services a day and delivers over 640 million customer journeys across its networks every year. 7,000 people joined the railway family in the last year

Major improvements are being delivered by DFTO train operators (TOCs) that are already under public ownership \- these are LNER, Northern, TransPennine Express (TPE), Southeastern, South Western Railway (SWR), c2c, Greater Anglia and WM Trains.

We work closely with the DfT but operate independently with our own governance and leadership teams. Our priority is ensuring efficient, dependable rail services for everyone.

Primary Purpose of Job:

The Principal Analytics Engineer is the technical leadership post within the DFTO Data function, and the data engineering authority 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.

The role combines hands\-on technical delivery with cross\-portfolio data engineering leadership. The postholder owns the data engineering approach across all active portfolio initiatives: setting the standards and patterns that govern how data products are built, holding the quality bar across a federated delivery model, and making the data engineering decisions that determine whether outputs from one initiative become reusable foundations for the next.

The postholder provides direct technical leadership and line management to the Analytics Engineers within the central DFTO Data team, as well as engineering leadership to the wider cross\-industry delivery community.

The environment is genuinely multi\-organisational. Each portfolio initiative has a named lead from that wider community — a senior data professional or functional analytics leader who owns the problem definition and stakeholder relationships. The Principal Analytics Engineer owns how those initiatives are built, to what standard, and how outputs from one initiative become reusable foundations for the next.

Key Responsibilties:

Cross\-portfolio engineering leadership

• Own the overarching technical approach across all active Common Data Service initiatives: set data engineering patterns, make authoritative source placement decisions, and ensure that what is built in one initiative is reusable across the portfolio rather than isolated within it.


• Manage the boundary between product leadership and data engineering leadership at the initiative level, working with initiative leads (data professionals from TOCs, NR, and RDG) in a peer relationship: receptive to product direction, firm on engineering approach, and clear about when a scoping decision carries architectural consequences that need to be surfaced and resolved.


• Sequence data engineering activity across concurrent initiatives, managing cross\-initiative dependencies and ensuring the central team's capacity is directed toward the work with the highest portfolio\-level return.


• Gate the recognition of shared, canonical data engineering artefacts (e.g., schemas, ingestion patterns, transformation models, and data products) that have reached the standard required to be reused across the portfolio rather than remaining initiative\-specific.


• Recognise when a delivery blocker is structural rather than technical (a data access gap, a governance ambiguity, a supplier contract problem) and escalate it to the appropriate function for resolution.

• 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.

• 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.

• 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.

• 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 harm

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