At a Glance
- Tasks: Lead data modelling and analysis for a transformative insurance data platform.
- Company: Join a forward-thinking insurance company embracing AI and cloud technology.
- Benefits: Competitive daily rate, remote-first work, and opportunities for professional growth.
- Other info: Dynamic role with potential for career advancement in a fast-paced environment.
- Why this job: Make a significant impact on data governance and AI initiatives in the insurance sector.
- Qualifications: Experience in data analysis, modelling, and strong SQL skills required.
The predicted salary is between 70000 - 90000 £ per year.
- Lead Data Analyst / Data Modeller
- Insurance Data Platform Programme
The Opportunity
Our client is undertaking a major transformation programme to modernise its data estate and establish a scalable cloud-based platform capable of supporting operational reporting, regulatory requirements, data governance and future AI initiatives.
We are seeking an experienced Lead Data Analyst / Data Modeller with a strong background in the insurance sector to play a key role in defining the enterprise data structures that will underpin the platform.
This position sits between business stakeholders, architects and engineering teams, requiring someone who can combine strong analytical capability with practical data modelling expertise.
You will be responsible for translating complex business and operational processes into structured, reusable data models that support multiple products, clients and downstream applications.
The environment includes modern cloud technologies, large-scale relational data sources and complex integration requirements across multiple systems.
Key Responsibilities
- Data Modelling & Design
- Lead the development of enterprise and subject-area data models across key insurance domains.
- Create conceptual, logical and physical data models that support current operational needs and future scalability.
- Define standardised data structures that can be reused across reporting, analytics and operational processes.
- Establish common definitions, attributes, hierarchies and relationships across multiple data sources.
- Ensure data models align with platform architecture and long-term business objectives.
- Collaborate with architects and engineers to ensure models are practical and implementable.
- Business & Data Analysis
- Work with SMEs and stakeholders to understand business processes, data challenges and reporting requirements.
- Investigate existing systems and identify opportunities for standardisation and rationalisation.
- Elicit, document and validate detailed data requirements.
- Translate business rules into structured specifications and transformation requirements.
- Identify data gaps, inconsistencies and quality issues across source systems.
- Support requirement workshops and challenge assumptions where necessary.
- Data Quality & Governance
- Define data quality standards, validation rules and reconciliation requirements.
- Support the creation of controls, monitoring processes and exception management frameworks.
- Help establish governance processes around data definitions, ownership and lineage.
- Drive consistency in metadata, reference data and business terminology.
- Ensure data outputs can support audit and regulatory obligations.
- Platform Delivery Support
- Partner with engineering teams to support ingestion, transformation and integration activities.
- Produce mapping documentation between source systems and target models.
- Validate that delivered solutions align with agreed business logic and data definitions.
- Support testing activities, data validation exercises and reconciliation processes.
- Contribute to solution design discussions across the broader data platform programme.
- Reporting & Analytics Enablement
- Define structures that support operational, management and regulatory reporting.
- Design data models that enable self-service capabilities and analytical use cases.
- Ensure data structures are capable of supporting future machine learning and AI initiatives.
- Assist in developing reusable datasets that reduce duplication and increase consistency across reporting outputs.
- Required Experience
- Proven experience working as a Senior Data Analyst, Lead Data Analyst or Data Modeller.
- Strong background within the Insurance industry.
- Demonstrable experience building enterprise, canonical or logical data models.
- Excellent understanding of relational databases and complex data structures.
- Strong SQL skills with the ability to analyse and profile large datasets.
- Experience working within cloud-based data platforms.
- Knowledge of ETL/ELT processes and modern data integration approaches.
- Experience working alongside data engineers, architects and business stakeholders.
- Strong understanding of data governance, data quality and reconciliation principles.
- Experience delivering within Agile environments.
- Excellent communication and stakeholder management skills.
- Technical Environment
Experience in some or all of the following is highly desirable
- AWS
- Databricks
- Delta Lake
- Lakehouse architectures
- Complex SQL environments
- ETL / ELT pipelines
- Change Data Capture (CDC)
- Data lineage and metadata management tools
- Reporting and analytics platforms
Desirable Background
- General Insurance, London Market, Specialty Insurance or Broking environments.
- Regulatory reporting and compliance-driven data programmes.
- Data migration or legacy modernisation initiatives.
- Enterprise-wide data governance projects.
- Consultancy or client-facing delivery experience.
- Experience supporting large-scale transformation programmes.
What We're Looking For
The successful consultant will be comfortable operating in a fast-moving delivery environment and capable of navigating complex stakeholder landscapes.
You'll be
- Commercially minded and outcome focused.
- Comfortable working with ambiguity.
- Highly analytical with excellent attention to detail.
- Confident influencing both technical and non-technical stakeholders.
- Able to challenge constructively and drive clarity.
- Equally comfortable discussing high-level architecture and field-level data definitions.
- Proactive in identifying risks, assumptions and dependencies.
- Skilled at translating complexity into practical solutions.
- #J-18808-Ljbffr
Senior Data Analyst - Insurance (AI & Data) employer: Nigel Frank
Join a forward-thinking organisation that prioritises innovation and collaboration, offering a dynamic work environment for a Senior Power Platform Developer. With a strong focus on employee growth, you will have the opportunity to lead projects, mentor fellow developers, and shape the technical direction of the company while enjoying the flexibility of a hybrid working model. This role not only provides competitive remuneration but also fosters a culture of continuous learning and development, making it an excellent choice for those seeking meaningful and rewarding employment in the tech industry.
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We think this is how you could land Senior Data Analyst - Insurance (AI & Data)
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We think you need these skills to ace Senior Data Analyst - Insurance (AI & Data)
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at Nigel Frank, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!
Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Nigel Frank, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
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How to prepare for a job interview at Nigel Frank
✨Showcase Your Analytical Skills
For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at Nigel Frank.
✨Brush Up on Technical Skills
You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
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Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Nigel Frank.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Nigel Frank.
