Data Engineer - FDT in Sheffield

Job Description

Level / Location: GCB4/5 — Various — co-located with the FDT Tech

Sit within the FDT Tech team and close to users (business teams, delivery pods, transformation squads) as the accountable data engineer for rapid discovery, shaping and delivery of production-viable data products that enable analytics/ML use cases. Build and iterate reliable datasets, pipelines and feature stores spanning ingestion, transformation, quality controls, lineage and provisioning—turning real user needs into trusted, repeatable data assets quickly, safely and in line with enterprise standards and platforms. This role bridges product intent and data engineering execution, accelerating time-to-value while ensuring data solutions are secure, supportable and reusable.

Reporting & Stakeholders

Solid line into FDT Tech Lead / Engineering Lead. Strong day-to-day partnership with Product Owner(s), business SMEs, analytics/DS colleagues and delivery pod leads on scope, prioritisation and trade-offs. Close collaboration with platform teams (e.g., AI platform / Data platform), architecture, and operations/support teams to ensure production readiness. Engage with tooling/platform owners to provide structured feedback and reusable patterns from field delivery.

Scope & Authority

Accountable for shaping, building and delivering datasets, pipelines and feature assets close to users, including ingestion, transformation, governance controls, quality and repeatable refresh. Owns local technical decisions required to deliver outcomes at pace, within approved architecture patterns, engineering standards, controls and governance frameworks. Authority includes recommending data solution patterns, implementing pipelines/optimisation, defining quality/lineage expectations, and escalating material risks, dependencies or control gaps.

Role Description & Core Accountabilities

This role exists because high-impact analytics/ML delivery often requires data engineers embedded with users to rapidly discover the right data, prove readiness via thin-slice datasets, and then harden and deliver reliable production pathways. The Data Engineer accelerates learning loops while maintaining engineering discipline, ensuring what’s built can scale, be supported and be reused.

• Use-case shaping with the business - Translate problem statements into data and analytics/ML-ready requirements with clear success metrics (KPIs, acceptance criteria). Define target population, decision points, value levers (cost, risk, customer experience), constraints and data implications.
• Data discovery and readiness - Identify required data sources (internal/external), assess availability, quality, lineage, bias and leakage risks. Partner with analytics/DS and business SMEs to confirm definitions, granularity, and time windows; document assumptions and gaps.
• Dataset provisioning & repeatable refresh - Work with data engineering and platform teams to get datasets provisioned, joined, and refreshed in a repeatable way. Build ingestion, transformation and orchestration patterns that support batch and (where needed) near-real-time requirements.
• Curated datasets & feature enablement - Create curated, analysis-ready datasets and feature sets (including point-in-time correctness where relevant). Implement data contracts, schemas, and feature computation pipelines aligned to modelling needs.
• Quality, observability & controls-by-design - Embed data quality checks, reconciliation, monitoring and alerting (freshness, completeness, drift proxies where applicable). Ensure secure handling, access controls, and auditability aligned with control requirements; raise risks early.
• Experimentation & measurement support - Enable A/B tests, holdouts, or quasi-experiments by provisioning appropriate populations, timestamps and tracking fields. Support incremental impact measurement through reliable data capture and consistent definitions.
• Deployment partnership - Package datasets/features with MLOps/engineering; define batch vs real-time data provisioning and SLAs. Specify monitoring (data quality, performance, fairness-relevant data signals) and operational thresholds; support production handover.
• Governance & responsible AI (practical implementation) - Document data purpose, limitations, lineage, quality controls, and known risks (e.g., bias/leakage). Support model reviews, validations, and audit artefacts in line with the relevant policy process.
• Stakeholder communication - Communicate trade-offs in plain language; keep delivery moving with crisp weekly milestones. Align business, DS and platform stakeholders on what’s “good enough now” vs what needs hardening.

Calibration Rationale

This is a hands-on, user-adjacent data engineering role optimised for speed-to-value and end-to-end delivery of trusted data assets for AI/ML. Core capabilities around engineering discipline, customer centricity, integration delivery and pragmatic decision-making are Essential. Broader strategic leadership and talent stewardship are Desirable, as the role contributes to scaling patterns and mentoring but does not typically own enterprise-wide strategy.

Experience — Required

• Proven experience delivering production data pipelines and curated datasets end-to-end (ingestion, transformation, orchestration and provisioning) in enterprise environments.
• Strong ability to rapidly prototype, iterate with users/analytics teams, and then harden data assets towards production readiness (repeatability, monitoring, support).
• Experience with data discovery: assessing availability, quality, lineage, bias and leakage risks; translating business definitions into data definitions.
• Working knowledge of secure engineering practices, testing, observability (logs/metrics), and operational handover for data products.
• Experience collaborating with product and business stakeholders to shape MVP scope and manage trade-offs using clear acceptance criteria and KPIs.
• Comfortable operating in ambiguous environments with shifting requirements, while maintaining delivery discipline and transparency.

Experience — Preferred

• Experience delivering within regulated/controlled environments (risk, audit, security, data handling) and navigating governance pragmatically.
• Experience with platform-based delivery and reuse (shared datasets, standard pipelines, feature platforms), including knowing when to reuse vs build.
• Experience with modern data integration patterns (batch/event-driven), data modelling for analytics/ML, and automation/orchestration tooling.
• Experience working in Value Streams / product-led delivery, transformation pods, or embedded engineering models.
• Experience contributing reusable accelerators (templates, reference pipelines, data quality frameworks) and coaching other engineers.

Behavioural Skills

• User-obsessed builder; stays close to users, validates fast, and focuses on measurable outcomes over perfect paperwork.
• Pragmatic engineering discipline; moves quickly without skipping fundamentals (security, testing, operability, controls).
• End-to-end ownership mindset; takes accountability from discovery through build, release and handover; doesn’t throw work “over the wall”.
• Bias for simplification and automation; eliminates manual steps and complexity; automates repeatable tasks wherever sensible.
• Clear communicator under pressure; explains options, trade-offs and risks in plain language; keeps stakeholders aligned.
• Collaborative and inclusive; works well across roles and backgrounds, welcomes different perspectives to get to better solutions faster.
• Proactive risk management; identifies integration, dependency, data and control risks early; escalates with options, not surprises.

Derailers / Red Flags

• Builds thin-slice datasets that cannot be industrialised, creating rework or hidden technical debt.
• Ignores security, controls, data quality or operational needs in the name of speed; pushes fragile data assets into production pathways.
• Over-builds or over-engineers instead of delivering thin-slice value and iterating.
• Creates bespoke point solutions where reusable platforms/patterns exist.
• Fails to manage dependencies or communicate risks early, causing delivery churn and stakeholder surprises.
• Focuses on delivery completion without ensuring supportability, monitoring and clear ownership.

Year 1 Outcomes (Scale & Embed)

• Consistent delivery of user-validated curated datasets/features that convert into production-viable, repeatable refresh pipelines with clear value realised.
• Multiple end-to-end data products delivered that measurably enable analytics/ML use cases (speed to insight, reduced manual effort, improved decisioning).
• Demonstrable reduction in cycle time from idea to analysis-ready data through embedded delivery and rapid iteration loops.
• Reusable components, patterns or templates contributed back to the Value Stream/platform communities, reducing duplication across teams.
• Strong production readiness discipline established (testing, security, observability, runbooks, handover), with reduced post-release incidents and rework.
• Clear, structured feedback provided to platform/tooling teams, influencing roadmap improvements based on field delivery realities.
• Recognised as a dependable embedded data engineering partner who unblocks delivery and raises overall data engineering maturity.