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Ascentia Partners

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Senior Data Engineer - London - £75,000-£85,000 - Hybrid - Insurance

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Summary

Senior Data Engineer for a digital-first UK home insurance provider, owning the data platform and end-to-end pipelines that power pricing analytics and underwriting. Day to day: building, validating and scaling batch ETL/ELT pipelines with SQL and Python; cloud and PySpark experience is a plus. Hybrid role in London.

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.

Skills

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See also

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