Data Engineering Manager

Someone needs to make sure the engineering actually ships, reliably, at scale, and to a standard that holds up under regulatory scrutiny. That's this role. You'll lead a team of data engineers, owning delivery end-to-end: pipelines, transformations, platform components, production operations, and the engineering practices that tie it all together. You'll set the bar for quality and ownership, manage cost and performance, and build a team culture where things are done properly, not just done. This is hands-on leadership. You won't be writing every pipeline, but you'll know what good looks like, hold people to it, and clear the path so they can deliver. You'll operate in a regulated financial services environment where auditability, resilience, and governance aren't optional extras, they're the baseline. What you'll be doing Leading a team of data engineers - hiring, coaching, developing, and setting clear expectations for delivery quality and ownership Owning end-to-end engineering delivery against the data product roadmap: pipelines that are idempotent, tested, observable, and documented Running production operations - monitoring, incident response, root-cause analysis, and ensuring issues are resolved with clear ownership and learning Defining and enforcing engineering standards across the team: coding conventions, testing strategy, CI/CD, code review, and documentation Owning the cost and performance profile of data infrastructure - actively optimising compute, storage, and resource utilisation Managing technical debt as a visible backlog item, not an invisible tax on delivery speed Partnering with Data Product Managers on priorities and trade-offs, and with the Principal Data Modeller on data model standards Ensuring engineering delivery meets regulatory, security, and governance requirements - auditable, recoverable, and secure by default About you 8+ years in data engineering or software engineering, with at least 2-3 years in people management Proven experience delivering and operating production data platforms and pipelines at scale Experience defining and enforcing engineering standards across a team Strong operational mindset: reliability, monitoring, incident response, cost management Hands-on with modern cloud data platforms (e.g. Snowflake, BigQuery, Redshift) Experience with orchestration tools (e.g. Airflow, Dagster), CI/CD, and automated deployment Confident influencing stakeholders and making delivery trade-offs with transparency Comfortable delegating - accountable for outcomes, not personal code output Demonstrated ability to build, grow, and retain high-performing engineering teams Experience in a regulated environment (financial services, insurance, or banking) Experience operating within a data product or platform operating model, not solely project-based delivery Desirable Experience with transformation frameworks (e.g. dbt) 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 Interview process Three stages: an initial screening call, followed by technical competency-based questions and a scenario task. Working schedule Based at our Bristol head office (BS1 5HL), 37.5 hours per week, Monday to Friday. We offer hybrid flexible working - a mix of office and home.