Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

Apron
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

About Apron

Apron was started by a group of people who’d spent years building products for some of today’s most successful global fintech companies. But there was one problem that no‑one was solving: Business money – the kind that buys tomatoes, tiles, and till rolls, keeps suppliers happy and businesses booming, and, before you know it, eats up your entire day.


One million small businesses in the UK spend 5 hours this week paying and reconciling invoices, alongside countless hours chasing staff for expense receipts. This problem is affecting entrepreneurs, dreamers, risk takers, the backbones of our communities. Imagine what they could do with this time instead. What would they build? How far could they go? That’s why we created Apron as an essential tech layer in the small business machine. We flip the payment experience from blocking business to boosting it, turning hours into minutes.


We have grown fast, expanding our team to circa 70 individuals across the UK, and are backed by Index Ventures, Bessemer Venture Partners, Visionaries Club, the founders of Melio and Klarna. We’ve raised $50m.


About The Team

At Apron, many teams rely on data – from Payments, Cards, and Invoice Capture to Customer Support. We’re also actively developing AI technologies, such as document recognition and invoice detection, that depend on a strong data foundation. We already have talented data analysts, and many others (product managers, engineers, support) who work with data every day. Our goal is to empower everyone at Apron to explore, understand, and act on data in a self‑serve way.


We built the first version of our data platform using dbt and Metabase. Now we’re taking it to the next level: evolving the architecture to handle more data sources, greater scale, and new use cases from analytics to AI. This includes building a warehouse setup that can grow with us, consolidating company‑wide data, and giving every team the tools to use it effectively.


We work on a hybrid basis, at least 3 days per week from our Liverpool Street offices.


What You’ll Be Doing

  • Build and run our data platform so it’s fast, reliable, and always ahead of the curve.
  • Make data self‑serve for the whole team – ensuring everything lands in our DWH with the right discovery & cataloging tools in place.
  • Set the rules of the road for data governance, privacy, and compliance (GDPR and beyond).
  • Keep our data safe by implementing and monitoring security controls.
  • Get your hands dirty – this is a hands‑on IC role where you’ll own the development and support of the platform.

What We’re Looking For

  • 5+ years across Data Engineering.
  • SQL & Python: schema design, transformations, query optimisation, automation, testing.
  • Track record of building ETL/ELT pipelines into modern warehouses (BigQuery, Snowflake, Redshift).
  • Familiar with tools like Dagster, Airflow, Prefect, dbt, Dataform, SQLMesh.
  • Cloud experience (we’re on GCP) + containerisation (Docker, Kubernetes).
  • Strong sense of ownership over data standards, security, and roadmap.
  • Previous startup/tech company/fast‑paced experience will be strongly preferred.
  • A collaborator at heart – working with analysts, engineers, and product teams to turn data into business impact.

What We Offer

  • Highly competitive salary
  • Stock options
  • Health insurance with AXA (including optical and dental cover)
  • Life Assurance with MetLife
  • Enhanced parental leave
  • Weekly Deliveroo allowance
  • Hybrid setup, with 3 days in the office (Liverpool Street, London)
  • Salary sacrifice schemes (nursery, cycle to work, electric vehicle)
  • Fully expensed tech

Seniority level

  • Mid‑Senior level

Employment type

  • Full‑time

Job function

  • Information Technology


#J-18808-Ljbffr

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.