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About the Role

Reporting to the Data Analytics Manager, this Senior Data Analyst role is a hands-on individual contributor focused on shifting the team's emphasis from platform delivery to value-added analytics and commercial insight. The role aims to build lasting data partnerships with underwriting, claims, and exposure management teams, embedding analytics into daily decision-making. The role holder will also apply and model technical standards, contribute to complex analyses, and introduce new techniques, including practical AI applications.

Responsibilities

Act as the senior analytics business partner to one or more underwriting classes, proactively identifying where analytics can improve pricing, portfolio steering, renewal decisions and exposure management.
Translate open-ended commercial questions into well-scoped analyses and dashboard requirements.
Collaborate with the wider business to identify areas of automation and acceleration to improve operational efficiency.
Identify practical opportunities to enhance the analytics workflow with AI tooling, such as using LLMs and agentic tools for data exploration, code generation, documentation, and surfacing insight to underwriters.
Support the Data Analytics Manager in shaping external data analytical propositions, from discovery and prototyping through to packaging and delivery.
Apply and help embed the team’s development lifecycle for BI and analytics assets, including naming conventions, documentation, peer review and sign-off before production release.
Follow and help maintain robust source-control practices for Power BI content, using PBIP / TMDL format, Git-backed repositories, feature branches, pull requests and meaningful commit history.
Contribute to Power BI workspace governance, including clear Dev / Test / Prod separation, deployment pipelines, dataset certification / endorsement, refresh monitoring and access management.
Coach analysts and BI developers on SQL, DAX, data modelling, visual design and engineering hygiene; participate in code / report reviews and knowledge-sharing sessions.
Work closely with Data Engineering on changes to the semantic / curated layer and data marts, ensuring analytics and business needs are reflected in platform design.
Deliver complex analytics workstreams end-to-end, including scoping, estimation, delivery, hand-over and post-implementation review.
Work within an Agile framework: break down requirements into epics and user stories, contribute to backlog prioritisation with the Data Analytics Manager, and provide realistic estimates.

Requirements

Well-developed and demonstrable experience in a data analytics or BI role, preferably in an insurance or finance environment.
Advanced SQL, including query optimisation and working with the Azure data stack (Data Factory, Synapse / Fabric, SQL-based semantic layers).
Advanced Power BI: data modelling (star schemas), advanced DAX, Power Query / M, performance tuning, RLS, and deployment via pipelines.
Understanding of Power BI engineering discipline: PBIP / TMDL source format, Git-based version control, pull-request review, structured release and rollback process; demonstrable experience of these practices in a team.
Proven ability to carry out peer review and QA of analytics work – spotting model errors, DAX issues, performance problems and UX weaknesses, and giving constructive feedback.
Excellent written and verbal communication skills, including presenting to underwriting and internal stakeholders.
Strong stakeholder engagement, with a track record of turning ambiguous business problems into delivered analytical outcomes.
Well-developed experience in a Lloyd’s syndicate, managing agent, London Market broker or specialty (re)insurer.
Python for data analysis (pandas / notebooks) and an appreciation of wider data science techniques, enough to collaborate credibly with Data Scientists and Actuarial.
Hands-on experience applying AI / LLM tooling to analytics work (e.g. Co-pilot for Power BI / Fabric, MCP-style integrations, agentic assistants, code-generation tools) with a pragmatic view on where they add value.
Experience with Microsoft Fabric, dbt, Azure DevOps / GitHub Actions, and data-quality tooling.

Skills

SQL
Azure data stack
Data Factory
Synapse
Fabric
SQL-based semantic layers
Power BI
Data modelling
DAX
Power Query
M
RLS
PBIP
TMDL
Git
Python
pandas
notebooks
AI tooling
LLMs
Microsoft Fabric
dbt
Azure DevOps
GitHub Actions
data-quality tooling

Location

Hybrid

Work Type

Full time
Hybrid

Experience Level

Senior

About the Company

AEGIS Values: Fairness and respect, Open and inclusive, Ambitious, Striving to be better, Investing in people’s potential.
AEGIS London is an equal opportunities employer and recognises the value of a diverse workforce in facilitating better decision making and business growth.
We encourage a variety of differing views, perspectives and insights to create a collaborative working environment.
Diversity and Inclusion are fundamental to our business and we encourage applications from all backgrounds recognising the diversity of society and our customers.
As a business, we understand individual circumstances may differ and aim to be adaptable and to support flexible working practices.
Talk to our recruitment team to understand how AEGIS London can help support you in reaching your full potential.

Equal Opportunity

AEGIS London is an equal opportunities employer and recognises the value of a diverse workforce in facilitating better decision making and business growth.
We encourage a variety of differing views, perspectives and insights to create a collaborative working environment.
Diversity and Inclusion are fundamental to our business and we encourage applications from all backgrounds recognising the diversity of society and our customers.
It’s important to us that you are able to perform at your best when applying for a role with AEGIS London. If there are any adjustments we can reasonably make to ensure that the process is accessible for you please telephone us on +44(0)20 7856 7856 or email recruitment@aegislondon.co.uk