Data Engineering Manager

The Data Engineering Manager leads the team responsible for building and operating the data pipelines, transformations, and platform components that deliver trusted data products and certified reporting across the organisation. You will own engineering delivery end‑to‑end – ensuring data is ingested, transformed, and served reliably, at scale, and to defined standards. You will set and enforce engineering practices across the team, manage production operations including monitoring and incident response, and actively manage platform cost and performance.

This is a hands‑on leadership role: you will line‑manage a team of data engineers, set clear expectations for quality and ownership, and build a culture of continuous improvement. You will work in a regulated financial services environment where auditability, resilience, and governance are non‑negotiable – and where the data you deliver powers executive decision‑making, regulatory reporting, and client‑facing outcomes.

• Own the end‑to‑end delivery of data pipelines, transformations, and platform components required to support the data product roadmap.
• Ensure pipelines are idempotent, recoverable, and production‑grade; tested at unit, integration, and data‑quality levels; observable with clear alerting and escalation paths; and documented to a standard that supports shared ownership.
• Manage delivery against sprint commitments, providing clear progress updates and early escalation of risks.
• Own production operations, including monitoring, alerting, incident triage, resolution, and root‑cause analysis; maintain runbooks and operational documentation.
• Manage on‑call or support arrangements where required, ensuring production issues are resolved with clear ownership, timelines, and learning.

• Own the cost and performance profile of data engineering infrastructure; actively monitor and optimise query and pipeline performance, compute and storage costs, and resource utilisation across environments.
• Make design and delivery decisions that balance performance, cost, and maintainability; manage technical debt as a visible backlog item.
• Partner with platform and technology teams on infrastructure evolution, capacity planning, and tooling decisions.

• Define, maintain, and enforce engineering standards, including coding conventions, naming standards, code review practices, testing strategy, and CI/CD and deployment practices.
• Ensure standards are practical, adopted, and reviewed – not theoretical documents that sit unused.
• Act as the engineering design authority for implementation decisions, in partnership with the Principal Data Modeller on data model design.
• Ensure consistency across squads where multiple engineers contribute to shared domains.

• Line‑manage, coach, and develop data engineers; set clear expectations for delivery quality, ownership, and professional standards.
• Build a high‑performing team culture focused on quality, craftsmanship, ownership, continuous improvement, and collaboration.
• Ensure the team has the right skills, capacity, and structure to meet roadmap commitments.
• Own hiring, onboarding, performance management, and career development; identify and address skill gaps through plans, hiring, or training.
• Ensure knowledge is distributed – actively reduce single points of failure.

• Partner closely with Data Product Managers, Principal Data Modeller, Data Governance, and Platform & Technology teams.
• Provide realistic delivery forecasts and make trade‑offs visible and explicit.
• Translate product requirements into engineering delivery plans with clear dependencies and sequencing.
• Escalate risks, blockers, and capacity constraints early and transparently; represent engineering perspective in roadmap planning and prioritisation discussions.

• Ensure engineering delivery meets regulatory, security, and governance requirements.
• Ensure data pipelines and platform components are auditable, observable, recoverable, and secure; support…