Principal Data Engineer

Arbor Education is dedicated to transforming the educational landscape by providing innovative management tools for schools. As a Principal Data Engineer, you will drive the architecture and technical strategy for data, ensuring efficient data flow from transactional systems to analytical insights while collaborating with various teams to achieve strategic business objectives.

Responsibilities:

• Define and evangelise target architectures that bridge our transactional, analytical, and AI systems, ensuring our data remains a competitive advantage
• Be a leader for our end-to-end data lifecycle, from OLTP schema design and migrations to OLAP governance, platform and analytics
• Partner with Technical Leads to provide hands-on guidance supporting feature delivery, tech debt paydown, and architecture improvements. (Hands-on for this role means staying close to the details but not necessarily coding)
• Collaborate with Product Directors to understand product vision and translate this into deliverables, aligning commercial and engineering goals
• Lead our shift to an AI-First way of working, architecting the data foundations to enable AI in our products and championing the latest approaches to help drive productivity gains
• Architect the future infrastructure required for LLM orchestration, Retrieval-Augmented Generation (RAG), and real-time model inference at scale
• Mentor and coach Technical Leads/Senior Engineers on architectural and engineering excellence, continually raising our bar and supporting career growth
• Act as a role model for technical leadership — work with pace, be pragmatic, and stay laser focused on outcomes not output
• Head up architectural reviews for your domain and ensure all designs meet Arbor’s quality, reliability, security and compliance standards
• Collaborate with Staff Engineers and Principal Engineers to shape Arbor’s broader technology strategy, working outside your domain as needed
• Partner with Engineering Management to help grow teams, identify and resolve systemic bottlenecks, ensuring the success of our strategic goals

Requirements:

• Extensive experience overseeing data architecture and a track record of delivering iterative outcomes across one or more OLAP domains
• Strong understanding of data modelling techniques (Star, Medallion, etc) and distributed data systems within cloud-native environments (Snowflake, etc)
• Experience in building and leveraging unstructured data across disparate systems to enable product development
• Examples of communication and influencing across technical and non-technical audiences to achieve positive outcomes
• An ability to support the growth of engineers and technical leads whilst still delivering value across distributed, cross-functional teams
• Demonstrable experience working in an ownership culture where quality, performance, observability, and security are core responsibilities for every engineer
• An understanding and opinion on how AI-first engineering will change the data ecosystem and ways of working, ideally with worked examples
• Examples of contributing to or leading technical communities of practice (e.g. chapters, guilds, architecture councils)
• Practical knowledge of DataOps principles, including CI/CD for data and the orchestration of complex pipelines (e.g., AWS, Debezium, Git, Infra as Code)
• Good commercial knowledge of data security standards, encryption at rest/transit, and relevant compliance laws
• Platform financial acumen - demonstrable experience of optimising SaaS platform spend, and the use of forecasting models
• AI/ML Engineering & MLOps, including model deployment/serving and monitoring
• Experience with federated learning and/or privacy-enhancing technologies (PETs)
• Direct experience with LLM Ops, including prompt engineering at scale, and token cost optimisation