Data Engineering Manager in Bristol

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'll own engineering delivery end-to-end - ensuring data is ingested, transformed, and served reliably, at scale, and to defined standards. You'll 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'll line‑manage a team of data engineers, set clear expectations for quality and ownership, and build a culture of continuous improvement. You'll 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.

Key Accountabilities

• Engineering Delivery & Operations
  • 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
  • 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 and alerting
  • Incident triage, resolution, and root‑cause analysis
  • Runbooks and operational documentation
  • On‑call or support arrangements where required
  • Ensure production issues are resolved with clear ownership, timelines, and learning.
• Platform Performance, Cost & Sustainability
  • Own the cost and performance profile of data engineering infrastructure.
  • Actively monitor and optimise:
  • Query and pipeline performance
  • Compute and storage costs
  • Resource utilisation across environments
  • Make design and delivery decisions that balance performance, cost, and maintainability.
  • Manage technical debt as a visible backlog item - not an invisible tax on delivery speed.
  • Partner with platform and technology teams on infrastructure evolution, capacity planning, and tooling decisions.
  • Define, maintain, and enforce engineering standards, including:
  • Coding conventions and naming standards
  • Code review and peer review practices
  • Testing strategy (unit, integration, data quality, regression)
  • CI/CD and deployment practices
  • Branching, versioning, and release management
  • Documentation and metadata requirements
  • 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.
• People Leadership & Capability
  • 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 and craftsmanship
  • Ownership and accountability
  • Continuous improvement and learning
  • Collaboration and knowledge sharing
  • 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 development plans, hiring, or training.
  • Ensure knowledge is distributed - actively reduce single points of failure.
• Stakeholder & Cross-Team Partnership
  • Partner closely with:
  • Data Product Managers (priorities, requirements, acceptance criteria, trade‑offs)
  • Principal Data Modeller (data model standards, canonical entities, transformation logic)
  • Data Governance (metadata, lineage, quality controls, access policies)
  • Platform & Technology (infrastructure, tooling, security)
  • 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.
• Governance, Risk & Regulatory Alignment
  • Ensure engineering delivery meets regulatory, security, and governance requirements.
  • Ensure data pipelines and platform components are:
  • Auditable - with clear lineage from source to consumption
  • Observable - with monitoring, logging, and alerting
  • Recoverable - with defined RPO/RTO and tested recovery processes
  • Secure - with appropriate access controls and data handling
  • Support audit, regulatory review, and operational risk assessments.
  • Ensure data retention, masking, and access control policies are implemented in code.
  • Partner with Data Governance to ensure all delivered assets meet cataloguing, metadata, and quality standards.

Experience & Skills

• 8+ years' experience in data engineering or software engineering roles, with at least 2-3 years in a people management capacity.
• Proven experience delivering and operating production data platforms and pipelines at scale.
• Experience working in a regulated environment (e.g. financial services, insurance, banking).
• Experience operating within a data product or platform operating model - not solely project‑based delivery.
• Strong understanding of data engineering principles and best practices.
• Hands‑on experience with modern cloud data platforms (e.g. Snowflake, BigQuery, Redshift, or equivalent).
• Experience with orchestration tools (e.g. Airflow, Dagster, or equivalent).
• Experience with CI/CD, infrastructure‑as‑code, and automated deployment practices.
• Experience defining and enforcing engineering standards across a team.
• Confident influencing stakeholders and making delivery trade‑offs with transparency.
• Clear communicator with both technical and non‑technical audiences.
• Comfortable delegating - accountable for outcomes, not personal code output.
• Demonstrated ability to build, grow, and retain high‑performing engineering teams.
• Experience with transformation frameworks (e.g. dbt or equivalent).
• Experience with streaming or event‑driven architectures.
• Exposure to semantic layers, metrics layers, or feature engineering patterns.
• Experience managing platform costs and optimising spend at scale.
• Familiarity with data governance tooling (catalogues, lineage tools, quality frameworks).
• Experience supporting AI/ML feature pipelines or model serving infrastructure.