Senior Data Engineer - Insurance - Remote

City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

The Senior Data Engineer will play a crucial role in designing, implementing, and maintaining scalable data pipelines and infrastructure. This position is ideal for those with strong technical expertise and a passion for working in the Insurance / Financial services industry.

Client Details

Senior Data Engineer

The employer is a medium-sized organisation operating in the F sector. They focus on delivering innovative solutions and maintaining a strong reputation for excellence in analytics and data-driven decision-making.

Description

Senior Data Engineer

Develop and maintain robust and scalable data pipelines and ETL processes.
Optimise data workflows and ensure efficient data storage solutions.
Collaborate with analytics and engineering teams to meet business objectives.
Ensure data integrity and implement best practices for data governance.
Design and implement data models to support analytical and reporting needs.
Monitor and troubleshoot data systems to ensure reliability and performance.
Evaluate and implement new tools and technologies to improve data infrastructure.
Provide technical guidance and mentorship to junior team members.Profile

Senior Data Engineer

A successful Senior Data Engineer should have:

Experience within the Insurance industry
Strong proficiency in programming languages such as Python, Java, or Scala.
Experience with cloud platforms like Azure.
Knowledge of big data technologies such as Hadoop, Spark, or Kafka.
Proficiency in SQL and database management systems.
Familiarity with data warehousing concepts and tools.
Ability to work collaboratively with cross-functional teams.
A solid understanding of data security and privacy standards.
A degree in Computer Science, Engineering, or a related field.Job Offer

Senior Data Engineer

Competitive salary ranging from £80,000 to £120,000 (Experience depending).
Equity options as part of the compensation package.
Comprehensive benefits package.
Opportunity to work remotely.
Be part of a collaborative and innovative team in the Insurance sector.If you are passionate about data engineering and are excited to work in a challenging and rewarding role, we encourage you to apply today

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.