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 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.

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; 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.
• 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, 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.
• Engineering Standards & Practices
  • 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.
• 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, 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.
• Stakeholder & Cross Team Partnership
  • 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.
• Governance, Risk & Regulatory Alignment
  • Ensure engineering delivery meets regulatory, security, and governance requirements.
  • Ensure data pipelines and platform components are auditable, observable, recoverable, and secure; support audit, regulatory review, and operational risk assessments.
  • Implement data retention, masking, and access control policies in code; partner with Data Governance to guarantee cataloguing, metadata, and quality standards.

Experience & Skills

• Essential
  • 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 (Snowflake, BigQuery, Redshift, or equivalent).
  • Experience with orchestration tools (Airflow, Dagster, or equivalent).
  • Experience with CI/CD, infrastructure as code, and automated deployment practices.
  • Experience defining and enforcing engineering standards across a team.
  • Strong operational mindset: reliability, monitoring, incident response, cost management.
  • Confidence 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.
• Desirable
  • Experience with transformation frameworks (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.

What's on offer?

• Discretionary annual bonus
• Annual pay review
• 25 days holiday plus bank holidays and 1 additional day Christmas closure
• Option to purchase an additional 5 days holiday (selectable during annual benefits window)
• Flexible working options, including hybrid working
• Enhanced parental leave
• Pension scheme up to 11% employer contribution
• Income protection and life insurance (4 salary core level of cover)
• Private medical insurance
• Health care cash plans - optical, dental, outpatient care
• Health screening programme
• Confidential support including mental health counselling and remote GP
• Wellhub - unlimited access to fitness providers and wellness coach sessions
• Variety of travel to work schemes with bike storage and shower facilities
• In house barista and deli serving subsidised coffee and sandwiches
• Two paid volunteering days per year

HargreavesLansdown is an inclusive employer that values diversity in its workforce. We encourage applications from all individuals without regard to race, religion, gender, sexual orientation, national origin, disability or age. This role may also be available on a flexible working or part time basis - please ask the Recruitment & Onboarding team for more information. Please note, we are unable to provide employment sponsorship to candidates.