At a Glance
- Tasks: Build and maintain data pipelines for pricing analytics in a dynamic insurance environment.
- Company: A modern, digital-first home insurance provider focused on data-driven solutions.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on innovation and scalability.
- Why this job: Join a passionate team and make a real impact on data-driven decision-making.
- Qualifications: Strong SQL and Python skills, with experience in ETL/ELT pipeline management.
The predicted salary is between 75000 - 85000 £ per year.
A modern, data-led, digital-first home insurance provider is seeking a talented Data Engineer to build and maintain the data foundations powering pricing analytics and underwriting performance. You do not need to have pricing specific experience, more the data engineering skills.
Role Overview
This is a highly autonomous, technically-focused role for someone passionate about data, code, and outcomes. You will design, build, and scale modular, end-to-end data pipelines, ensuring data quality, consistency, and scalability. Collaborating with Pricing, Underwriting, Data Science, and Engineering teams, you will enable smarter, faster decision-making by providing accurate, reliable, and timely pricing insights.
Key Responsibilities
- Own and evolve the data platform and pipelines for pricing analytics.
- Implement governance, validation, and alerting to ensure data integrity and reliability.
- Consolidate and modularise code for reusable, maintainable data components.
- Support batch processing for re‑priced datasets to deliver timely pricing and underwriting insights.
- Collaborate with teams to align on engineering standards and best practices.
- Streamline processes and enhance the scalability of the pricing data ecosystem.
Requirements
- Strong experience in SQL and Python (or similar object-oriented language).
- Proven experience designing and managing ETL/ELT pipelines.
- Meticulous attention to detail with a focus on data accuracy and process reliability.
- Self‑starter with strong problem‑solving, analytical, and communication skills.
Highly Advantageous
- Experience with cloud platforms (e.g., Azure, AWS, or GCP).
- Familiarity with PySpark or other big data technologies.
- Understanding of version control (e.g., Git).
- Knowledge of pricing or modelling workflows and how engineering choices affect model performance.
Senior Data Engineer - London - £75,000-£85,000 - Hybrid - Insurance employer: Ascentia Partners
As a modern, data-led home insurance provider based in London, we pride ourselves on fostering a dynamic and inclusive work culture that champions innovation and collaboration. Our employees enjoy competitive salaries, hybrid working arrangements, and ample opportunities for professional growth, all while contributing to meaningful projects that enhance decision-making in the insurance sector. Join us to be part of a forward-thinking team where your expertise in data engineering will directly impact our success and drive positive change in the industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer - London - £75,000-£85,000 - Hybrid - Insurance
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We think you need these skills to ace Senior Data Engineer - London - £75,000-£85,000 - Hybrid - Insurance
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 Ascentia Partners, 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 Ascentia Partners. 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 Ascentia Partners
✨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!
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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 Ascentia Partners!
✨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.
