National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Senior Data Engineer

Workable
Greater London
1 week ago
Create job alert

About Wrisk

Wrisk is reinventing insurance for today’s digital consumer and helping an outdated industry become relevant again in the process. In the same way that fintech companies have disrupted the traditional banking sector, reimagining financial platforms for a new generation, Wrisk’s founders share a vision for how insurance ought to be: simple, transparent and personal. Bringing together two disparate industries (technology and insurance), they have created an insurance experience like no other, centred squarely around the customer.

The result is Wrisk: flexible insurance that adapts to fit your life. Our mobile-first, frictionless platform lets people interact with their insurance provider with the same ease, speed and transparency they’re already used to having with providers in other sectors. Customers can pay monthly, instantly make changes to their cover and bring all their disclosure, payment and claim information together in a single place.

Now, with some big brand partners, we are bringing our unique customer experience and platform to market to change how insurance is bought, sold and managed.

What we are looking for…

We're looking for a Senior Data Engineer who thrives on autonomy, is obsessed with clean, scalable data systems, and takes pride in building infrastructure that lasts. You'll be working closely with our existing Senior Data Engineer (your direct report) to tackle ambitious projects across our AWS data stack—designing, building, and improving pipelines, services, and infrastructure to power Wrisk’s growth.

This is a hands-on role for someone who prefers shipping high-quality code over spending time in meetings, and who can translate abstract or high-level problem statements into structured plans and working solutions independently.

You’ll be expected to lead by example—delivering clean, robust code, championing best practices, and raising the bar for technical excellence across the data engineering function.

 

 

What you’ll do…

  • Build and Own Data Infrastructure: Design, implement, and maintain cloud-native data infrastructure on AWS (ECS, ECR, RDS, Redshift, API Gateway, EC2, networking).
  • Develop Data Pipelines: Architect and maintain robust data pipelines and workflow systems using Python and orchestration tools.
  • Develop Services: APIs using fast api, making and deploying simple front ends, making use of modeling techniques and LLMs
  • Infrastructure as code (Terraform): Manage infrastructure as code using Terraform to ensure reproducibility, security, and scale.
  • Solve Ambiguous Problems: Work from abstract requirements to actionable plans and implement solutions independently.
  • Write Clean, Future-Proof Code: Create modular, well-tested code that adheres to industry standards, prioritising long-term maintainability over quick fixes.
  • Ensure Data Integrity and Performance: Optimise queries, schema designs, and storage strategies to ensure reliable and efficient data flows.
  • Improve and Automate: Proactively Identify bottlenecks, improve operational efficiency, and help drive automation across the stack.
  • Collaborate, But Own It: Collaboration is very welcome and encouraged. Be open to ideas and don’t hesitate to champion your own, but at the same time you’ll be trusted to lead your area of work without micromanagement.

Requirements

About You…

  • Minimum of 5 years of experience across Data Engineering and back end development roles.
  • You’re comfortable context switching and know how to find the balance between managing delivery pressure and maintaining quality.
  • Strong Python development experience (you can write well-structured, reusable code).
  • Infrastructure as code proficiency ideally terraform (you understand IaC and have deployed infra this way).
  • Strong analytical capabilities for working with unstructured datasets, performing root cause analysis, and identifying areas for improvement.
  • Strong AWS knowledge, particularly with services like ECS, EC2, ECR, RDS, Redshift, S3, API Gateway, networking and load balancing.
  • Deep understanding of SQL and fundamental database concepts (indexes, schema design, columnar etc).
  • Familiarity with DAG-based orchestration tools (e.g., Airflow or similar).
  • Advanced “data plumbing” skills – ELT ETL, etc
  • Familiarity with containerisation tools such as Docker.

Additional Considerations:

  • Experience in driving growth within an early-stage startup is advantageous but not required.
  • Prior experience in the financial/insurance services sector will be a plus but not required

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer (SQL Server / AWS)

Senior Data Engineer - Snowflake - £100,000 - London - Hybrid

Senior Data Engineer

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs UK 2025: 50 Companies Hiring Now

Bookmark this guide—refreshed every quarter—so you always know who’s really expanding their data‑science teams. Budgets for predictive analytics, GenAI pilots & real‑time decision engines keep climbing in 2025. The UK’s National AI Strategy, tax relief for R&D & a sharp rise in cloud adoption mean employers need applied scientists, ML engineers, experiment designers, causal‑inference specialists & analytics leaders—right now. Below you’ll find 50 organisations that have advertised UK‑based data‑science vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the kind of employer—& culture—that suits you. For every company you’ll see: Main UK hub Example live or recent vacancy Why it’s worth a look (tech stack, mission, culture) Search any employer on DataScience‑Jobs.co.uk to view current ads, or set up a free alert so fresh openings land straight in your inbox.

Return-to-Work Pathways: Relaunch Your Data Science Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like stepping into a whole new world—especially in a dynamic field like data science. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s data science sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve gained and provide mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for data science talent in the UK Leverage your organisational, communication and analytical skills in data science roles Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to data science Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to data science Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as a data analyst, machine learning engineer, data visualisation specialist or data science manager, this article will map out the steps and resources you need to reignite your data science career.

LinkedIn Profile Checklist for Data Science Jobs: 10 Tweaks to Elevate Recruiter Engagement

Data science recruiters often sift through dozens of profiles to find candidates skilled in Python, machine learning, statistical modelling and data visualisation—sometimes before roles even open. A generic LinkedIn profile won’t suffice in this data-driven era. This step-by-step LinkedIn for data science jobs checklist outlines ten targeted tweaks to elevate recruiter engagement. Whether you’re an aspiring junior data scientist, a specialist in MLOps, or a seasoned analytics leader, these optimisations will sharpen your profile’s search relevance and demonstrate your analytical impact.