Analytics Engineer — Power BI & Databricks

Canopius Group

Manchester

Hybrid

GBP 55,000 - 75,000

Full time

2 days ago

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Job summary

Canopius Group in Manchester is seeking an analytics engineer to build and maintain analytics and reporting capabilities using Power BI and Databricks. You will translate business problems into clear requirements and deliver reliable solutions within a multi-disciplinary team.

The role focuses on data modelling, reporting, and turning a governed data estate into trusted self-service insights, with hybrid working and collaboration with data and business stakeholders across Canopius.

Qualifications

Hands-on development with Power BI, DAX, semantic models and paginated reports.
Knowledge of analytics engineering, semantic/dimensional modelling and cloud data platforms.
Experience building clear, reliable and user-friendly reports and dashboards for decision making.
Strong SQL skills; familiarity with Azure DevOps, Git, and CI/CD practices.
Experience in Agile or Scrum environments and cross-functional teamwork.
Analytical, detail-oriented problem solver with continuous improvement mindset.
Strong communication to explain analysis and solution choices.
Experience with AI-assisted development tools and copilots is advantageous.
Familiarity with Tabular Editor and Databricks desirable.
Familiarity with Lloyd's market data is advantageous.

Responsibilities

Develop analytics engineering and reporting solutions that help business users access trusted insight and make informed decisions.
Work with stakeholders to understand information needs and translate into clear, deliverable requirements.
Design, build and maintain semantic models, datasets and reporting solutions using Power BI and Databricks.
Support report and dataset performance by reviewing data models, DAX, queries and refresh approaches.
Apply data validation, reconciliation and testing to ensure analytics outputs are accurate.
Document solutions including data sources, logic, refresh schedules and access requirements.
Follow team standards for modelling conventions, naming, testing, deployment and version control.
Rationalise and redevelop legacy reporting as part of migration to modern platforms.
Communicate analytics outputs, assumptions, risks and limitations clearly to audiences.
Plan and manage assigned work items with realistic estimates, dependencies, risks.
Participate in peer reviews, knowledge sharing and team ceremonies.
Use modern analytics engineering practices and AI-assisted tools to improve delivery quality.
Keep abreast of developments in data, analytics and reporting technology.
Manage own task list and priorities.
Undertake other ad-hoc duties as required.