Data Engineer

Propel
London
4 months ago
Create job alert

🚀 We’re Hiring: Data Engineer I | 4 x days onsite Victoria, London | £65–£75k + Equity


We’re excited to be partnering with a fast-growing fintech building a next-generation co-brand card platform for leading travel and hospitality brands like Marriott International and Hilton.


📍 Location: London Victoria (4 days in office, 1 from home)

💰 £65–£75k p.a. + Equity

🎓 2:1+ STEM degree (Russell Group)


I'm hiring a Mid-Level Data Engineer (3+ years’ experience) to help scale and optimise a robust data platform built on Python, Airflow, dbt and Redshift.


🔧 What you’ll be doing:


• Scaling and optimising ELT pipelines (Python + Airflow)

• Enhancing the data warehouse (performance, cost, complexity)

• Expanding Reverse ETL and data activation capabilities

• Deepening dbt modelling and data quality frameworks

• Driving automation, governance and reliability across AWS

• Collaborating closely with Product, Design, Marketing & Finance


🎯 What we’re looking for:


• 3+ years in Data Engineering (FinTech/startup/scale-up ideal)

• Strong Python + SQL expertise

• Production-grade workflow orchestration (Airflow/Dagster/Prefect)

• Deep understanding of data warehousing + ELT principles

• Interest in Open Banking & payments


This is a pivotal role at a high-growth fintech entering its next scaling phase perfect for someone who thrives on optimisation, ownership and impact.

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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 Engineering Employers to Watch in 2026: UK and Global Companies Driving the Data Revolution

Data engineering is at the heart of the digital economy, transforming raw data into actionable insights, powering analytics, AI systems, and cloud infrastructure. As the UK and global markets continue to invest heavily in data platforms, pipelines, and real-time analytics, demand for skilled data engineers is growing rapidly. For professionals exploring opportunities on www.DataEngineeringJobs.co.uk , the critical question is: which companies are expanding, hiring, and shaping the future of data-driven business? This article highlights new data engineering employers to watch in 2026, including UK startups, scale-ups, and international firms expanding in the UK.

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

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

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

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.