Lead a team of analytics engineers to enhance data modelling and delivery at Collinson. Collaborate across departments to ensure scalable and consistent data strategies.
Responsibilities
Lead, mentor, and inspire a high-performing team of analytics engineers.
Foster data craftsmanship by embedding standards, training, and structured career development.
Co-own end-to-end data delivery from ingestion through to the analytical layer.
Champion collaboration across data engineering, analytics, governance, and product teams.
Define and implement best practices in data modelling, naming conventions, documentation, and business logic.
Lead the creation of subject-area models and semantic layers that power reporting, AI, and personalisation.
Drive traceability, clarity, and trust in analytical data products — ensuring consistent use across the business.
Translate complex business concepts into elegant, reusable data assets.
Shape and deliver against the Data & Analytics roadmap, ensuring scalable analytical engineering practices.
Represent analytical engineering in cross-functional forums, advocating for consistency, lineage, and alignment.
Partner with data governance, data science, and BI teams to ensure analytical assets support both operational and strategic insight.
Influence platform decisions alongside technical peers, ensuring scalability and sustainability in design.
Requirements
Proven leadership in analytics or data engineering teams within a modern cloud-based data platform.
Strong foundation in data modelling principles, such as dimensional modelling and domain-driven design.
Experience developing and governing semantic or analytical layers for enterprise analytics.
Excellent communication and stakeholder management skills across business and technical teams.
A deep passion for simplicity, clarity, and maintainability in data design.
Proven ability to co-lead delivery and architecture with technical partners.
Exposure to data contracts, metadata management, or governance frameworks (desirable).
Experience supporting AI, machine learning, or personalisation use cases (desirable).
Understanding of Agile delivery in modern data teams (desirable).
Knowledge of how analytical engineering integrates into data product and self-service analytics strategies (desirable).
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