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

Apply Now

Data Engineer at well-funded AI cybersecurity startup

Jack & Jill
London
2 days ago
Create job alert

This is a job that we are recruiting for on behalf of one of our customers.

To apply, speak to Jack. He's an AI agent that sends you unmissable jobs and then helps you ace the interview. He'll make sure you are considered for this role, and help you find others if you ask.

Data Engineer

Company Description: Well-funded AI cybersecurity startup

Job Description:

As a Backend Engineer (Data Engineering), you'll architect and build production-grade data pipelines for a well-funded AI cybersecurity startup. This role involves designing and implementing data lake architecture, streaming pipelines, and transformation systems to process massive volumes of security data. You'll own the entire data lifecycle, ensuring reliability and performance to power AI agents protecting customer environments.

Location: Remote

Why this role is remarkable:

  • Ambitious data challenges at the intersection of generative AI and cybersecurity, building systems for proactive threat detection.
  • Join a well-funded startup backed by top-tier VCs, with a team of experienced leaders from Big Tech and Scale-ups.
  • Opportunity to build an AI-native company from the ground up, architecting the data foundation using cutting-edge technologies like Apache Iceberg.

What you will do:

  • Design, implement, and maintain scalable data pipelines that ingest gigabytes to terabytes of security data daily, processing millions of records rapidly.
  • Architect and evolve S3-based data lake infrastructure using Apache Iceberg, creating distributed systems for efficient storage and transformations.
  • Take end-to-end ownership of the complete data lifecycle, from Kafka ingestion to Spark/EMR transformations, enabling AI-powered analysis.

The ideal candidate:

  • 7+ years of software engineering experience with at least 4+ years focused specifically on data engineering, demonstrating strong software engineering skills.
  • Proven track record building and scaling data ingestion systems handling gigabytes to terabytes daily, with experience at companies moving massive data volumes.
  • Deep, hands-on production experience with Python, Apache Kafka, and Apache Spark, using these technologies intimately.

How to Apply:

To apply for this job speak to Jack, our AI recruiter.

Step 1. Visit our website

Step 2. Click 'Speak with Jack'.

Step 3. Login with your LinkedIn profile.

Step 4. Talk to Jack for 20 minutes so he can understand your experience and ambitions.

Step 5. If the hiring manager would like to meet you, Jack will make the introduction

Related Jobs

View all jobs

Lead Data Engineer SQL Python

Data Engineer - Mid Level

Consultant - Manager, Data Engineer, AI & Data, Defence & Security

Test Data Engineer

Databricks Data Engineer

Data Engineer - Ms Dynamics 365

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

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.