At Canopius, our delivery teams are responsible for ensuring that business users can effectively harness data insights to drive strategic decision making. Our data strategy is centred around an enterprise Lakehouse platform on Databricks, avoiding fragmented, ungoverned silos on legacy technologies that hamper creativity and scalability. We are building a governed, interoperable data estate that enables self-service for our business teams and provides the trusted foundation for advanced analytics, machine learning and AI to accelerate change across our industry.
This role is an opportunity to apply your expertise in analytics engineering, data modelling and visualisation to build, extend and maintain the analytics and reporting capabilities that are central to decision making across Canopius. You will contribute to key business transformation projects, working closely with senior business stakeholders and leveraging modern technologies such as Power BI and Databricks. As a senior member of the team, you will also act as a role model and mentor to other engineers, providing guidance and support.
The ideal candidate is an experienced analytics engineer looking for a new challenge who is enthusiastic about using technology to accelerate change within our industry. You should have the ability to understand business problems and deliver efficient and reliable solutions tailored to our unique challenges, while keeping to a high degree of technical excellence and good analytics engineering practices. You should be comfortable collaborating and working as part of a dynamic multi-disciplinary team.
This role is central to delivering Canopius’ data strategy by turning a governed, interoperable data estate into trusted, self-service insight. Alongside hands-on delivery, the role provides technical leadership across analytics initiatives, helping to establish a future-ready, cloud-native reporting ecosystem and supporting the transition of legacy reporting onto modern platforms
Key Responsibilities
- Provide technical leadership and direction across analytics engineering and reporting initiatives.
- Work with the business to understand information needs and agree the best way to deliver trusted insight through reporting and analytics solutions.
- Design, build and optimise semantic models, datasets and reporting solutions using Power BI, paginated reports and Databricks, ensuring alignment with team development standards.
- Monitor and optimise report and dataset performance, tuning data models, DAX calculations, queries and refresh strategies to deliver fast, efficient and scalable analytics.
- Implement data validation, reconciliation and automated testing across analytics solutions to ensure trusted, accurate outputs.
- Design solutions for production operation, including documentation, monitoring, refresh, logging, access and defined support responsibilities, ensuring that they are secure, scalable, maintainable and aligned with organisational standards and governance requirements.
- Apply and evolve team standards (such as modelling conventions, naming, testing, deployment) and ensure consistency across the analytics codebase, identifying when to introduce new patterns versus reuse existing ones.
- Rationalise and redevelop legacy reporting solutions as required, supporting their migration onto modern platforms.
- Communicate technical solutions, trade-offs, risks and opportunities to both technical and non-technical audiences, building trusted stakeholder relationships, managing expectations and influencing decisions with evidence-based recommendations.
- Lead technical planning, breaking complex work into deliverable increments, producing realistic estimates and highlighting dependencies and risks early.
- Collaborate with Product Owners, Business Analysts and Solution Architects to define product vision and analytics roadmaps.
- Drive adoption of modern analytics engineering practices, AI technologies and automation opportunities across the team.
- Drive delivery of complex, end-to-end work items, seeking clarification of ambiguous specifications and proactively flagging risk of technical debt.
- Mentor and support other team members through pairing, peer review of pull requests, constructive feedback and knowledge sharing, acting as a role model to the team.
- Keep abreast of developments and trends in data, analytics and reporting technology.
- Management of own task list and ensuring that plans are agreed.
- Undertake other ad-hoc duties as required.
Skills & Experience
- Strong understanding of analytics engineering principles, semantic/dimensional modelling and cloud-based data platforms.
- Significant hands-on development experience with Power BI, including DAX, tabular/semantic models and paginated reports.
- Experience optimising report and model performance, including tuning DAX calculations and data models, using tools such as Performance Analyzer, DAX Studio and VertiPaq Analyzer.
- Strong SQL skills for building and optimising data transformations at scale.
- Experience with Azure DevOps, including branching and release/deployment strategies; Git, CI/CD and DevOps Pipelines.
- Demonstrable experience supporting internal Analytics, BI and reporting functions in a commercial, cloud-hosted environment.
- Experience working in an Agile and Scrum environment, with proven ability to work effectively in cross-functional teams.
- Experience leveraging AI-assisted development tools and copilots to improve productivity and delivery outcomes is advantageous.
- Strong analytical and problem-solving skills with a continuous improvement mindset; sets and maintains development standards and drives efficiencies in the end-to-end delivery process.
- Good communication and presentation skills, with the ability to explain complex topics in an easy-to-understand manner for both technical and non-technical audiences, and translate ambiguous business needs into solutions.
- Excellent cross-functional team player, able to work with stakeholders at all levels and across all business functions.
- Familiarity of working with Tabular Editor is desirable.
- Hands-on Databricks experience, including building curated, well-modelled datasets on a medallion/Lakehouse architecture is advantageous.
- Familiarity with specialty (re)insurance or Lloyd’s market data such as bordereaux, delegated authority, underwriting and claims is advantageous.