Data Engineer

Venquis
London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Looking for a Data Engineer to join the Data Engineering team.


The team is responsible for ensuring the delivery of accurate, curated data for downstream consumption in a timely manner, and are responsible for extracting data from source solutions and transforming that into a central data repository The role will be working closely with the Data Analytics team, providing assistance where required on producing any required outputs needed by the business.


Perform the day to day running of the ETL processes that feed into the central data repository.


Work with key stakeholders and other teams to gather requirements, identify where the data is located and to then implement the required changes.


Continue to improve the existing processes, including optimization and maintenance improvements.


Diagnose any issues with the data, the processing of that data and any associated code.


Work with other teams as a subject matter expert for the data model and associated lineage.


To ensure that robust data quality checks are embedded in the code to reflect established business processes.


To support the Data Analytics team with data reconciliation issues.


Working with the overall team to assist in migrating to the cloud


Skills

Strong Python and SQL coding skills with a good understanding of SDLC. Knowledge of cloud technologies and how to use them in a data and reporting solution. The ability to analyse, refactor and implement processes (technical/business). Good technical understanding of developing data pipeline solutions abiding to best practice. Good communication skills. Ability to document and accurately capture business requirements, translating that into a Technical Solution Specification document detailing the data engineering solution (the ability to translate business requirements into data requirements). Good understanding of data warehousing concepts. • Good understanding of data modelling techniques. Understanding of business operations and Insurance industry trends • Problem-solving: The ability to identify and analyse complex problems, generate solutions and debug complex code / data packages

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.