At a Glance
- Tasks: Lead the development of a cutting-edge Snowflake data platform and ensure data governance.
- Company: Join Willis Re, a forward-thinking tech-driven reinsurance company.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Make a real impact in shaping data solutions for the reinsurance industry.
- Qualifications: Proven experience in data engineering and deep expertise in Snowflake.
The predicted salary is between 75600 - 92400 £ per year.
Willis Re is building its global technology estate from the ground up, unencumbered by legacy and designed around data, analytics and modern cloud platforms.
Our Snowflake data lake platform sits at the centre of that estate, and we are looking for a Data Engineering Lead to drive its implementation.
This is a deeply hands‑on role.
You will spend most of your time building on Snowflake and establishing how data is tagged, catalogued, governed and quality‑assured across the business, working alongside our architects and directing strategic delivery partners who build with you.
Key Responsibilities
- Snowflake
Development: Spend the majority of your time hands‑on in Snowflake, architecting and building the platform and its surrounding ecosystem, including ingestion, transformation, data models, curated data products, and warehouse, performance and cost design for high‑volume reinsurance placement, exposure, claims and market data.
- Tagging & Cataloguing: Establish and maintain data tagging, classification and cataloguing, so that data across the platform is discoverable, well described and correctly labelled for sensitivity and business meaning.
- Data
Governance: Own governance for the platform, covering ownership and stewardship, lineage, access policies, retention obligations and cross‑border data residency, working with security, risk and the business.
- Data Quality: Define and implement data quality controls, automated testing, monitoring and reconciliation, and make quality visible and measurable to data consumers.
- Scale & Archival: Design the platform to handle growing data volumes predictably, defining partitioning and clustering, storage tiering, retention and archival strategy, and keeping performance and cost under control as the estate grows.
- Architecture
Contribution: Contribute to the data architecture, working with the Solution Architect and Head of Architecture & Engineering to shape target‑state designs and feed real‑world constraints back into them.
- Vendor Leadership: Direct and quality‑assure the work of strategic delivery partners, reviewing their designs and code and holding them to the agreed standards.
- Engineering Standards: Set how the team builds, covering CI/CD for data, automated testing, observability and infrastructure‑as‑code.
- Grow the Team: Mentor engineers, raise the technical bar, and help shape how the data engineering function scales.
Experience
- Proven track record in data engineering, including experience leading the delivery of a significant data platform end to end.
- Deep, current, hands‑on Snowflake expertise, having both built and architected across the platform and its surrounding ecosystem.
This includes warehouse sizing, performance and cost optimisation, RBAC and access design, object tagging and masking policies, ingestion (Snowpipe, Streams and Tasks), sharing and marketplace, AI and ML capabilities such as Cortex, and transformation and orchestration tooling such as dbt.
- Strong track record establishing data governance in practice, covering tagging and classification, data catalogues, lineage, stewardship and access policies.
- Practical experience implementing data quality frameworks, automated testing and monitoring, and driving measurable improvement.
- Deep expertise in data modelling and warehouse design, with sound judgement on schema design, performance and cost at high volume.
- Proven experience running data platforms at high volume, including partitioning and clustering strategies, storage tiering, and defining retention and archival approaches that satisfy long‑term regulatory obligations without runaway cost.
- Strong SQL and Python, with practical experience of pipeline orchestration, ELT tooling, CI/CD (Azure Dev Ops or Git Hub Actions) and infrastructure‑as‑code (Terraform/Bicep) on Azure.
- Enough architectural depth to shape platform design and challenge it constructively, and experience building to security and compliance requirements in regulated financial services.
- Experience leading or quality‑assuring work delivered by vendors and partners, including the ability to challenge designs constructively and enforce standards without direct authority.
- Hands‑on experience with Snowflake Cortex AI, or building AI and analytics use cases on data you have modelled yourself, is a significant advantage.
Familiarity with AI‑assisted engineering tools such as Claude Code or Claude Cowork is a plus.
- Ability to lead engineers, influence stakeholders and explain technical trade‑offs clearly to both technical and business audiences.
- About Willis Re
We combine specialist broking with analytics, modelling and research to help insurers optimise risk transfer, strengthen balance sheets and achieve sustainable growth.
Our approach is relationship‑driven, transparent and outcome‑focused.
At the heart of Willis Re is a focus on delivering the most cutting‑edge analytical solutions to enable more informed, better decision‑making for risk selection, portfolio optimisation, and capital management.
The launch of Willis Re brings a strategic advantage of being unhindered by legacy, an ability to leverage data, statistical models and advanced technologies with the best knowledge and expertise to deliver more efficient and effective reinsurance outcomes.
This places Willis Re in a unique position to build a truly analytically driven business, focused on creating solutions for the reinsurance industry that are future‑led and forward thinking.
Willis Re will also leverage recognised technical expertise from WTW’s Insurance Consulting & Technology business including their advanced modelling and analytical capabilities.
Alongside this will be WTW’s Research Network, an award‑winning business supporting and influencing science to improve the understanding and quantification of risk.
We’re hiring people from all walks of life who are curious, collaborative and ambitious.
We provide equal opportunity to all qualified individuals regardless of race, colour, religion, age, gender, gender expression, national origin, veteran status, disability, orientation, or any other legally protected categories.
If you have a need that requires accommodation, please email us at talentacquisition@willisre. com
#J-18808-Ljbffr
Data Engineering Lead employer: Willis Re (UK) Limited
Willis Re is an exceptional employer that fosters a collaborative and innovative work culture, where Capital Advisory Specialists can thrive. With a strong emphasis on professional development, employees are encouraged to enhance their skills through diverse projects and cross-functional teamwork, all while enjoying the dynamic environment of the insurance capital markets. The company's commitment to integrity and excellence ensures that team members are well-supported in delivering high-quality advisory solutions to clients.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineering Lead
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Willis Re (UK) Limited!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Engineering Lead at Willis Re (UK) Limited.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Willis Re (UK) Limited.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineering Lead at Willis Re (UK) Limited, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Engineering Lead
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Willis Re (UK) Limited, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Willis Re (UK) Limited. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Willis Re (UK) Limited
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Willis Re (UK) Limited!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.