Staff Data Engineer

Harnham
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior AWS Data Engineer & Trusted Consultant

Principal Data Engineer FTC

Staff Data Scientist

Principal Data Engineer

Data & Analytics Data Quality Engineer

DATA ENGINEER

LOCATION: London or Paris (remote-first with occasional meets)

SALARY: €120,000 + Equity


Shape the future of AI safety by building data pipelines at massive scale. Your work will power innovation across research and production.


THE OPPORTUNITY


The company is a fast-growing AI safety organization focused on building the reliability and optimization layer for AI systems. Its core platform uses natural-language policies to define what AI models should and shouldn’t do, automatically testing and enforcing these rules at scale.


Backed by $11M in funding from leading investors and senior figures at major AI and tech firms, the company processes over 100 million API calls monthly. It also fine-tunes and trains its own large language models to deliver faster and more cost-effective performance than open or proprietary alternatives.


The team is small, highly focused, and committed to solving hard problems with real-world impact. Employees work in a collaborative environment where ideas ship quickly to production and directly influence the future of AI safety.


ROLE AND RESPONSIBILITIES


This role focuses on designing and building scalable data pipelines and environments to handle petabytes of logs, events, and model outputs. You’ll develop internal APIs and tools that enable engineering and research teams to access data seamlessly without infrastructure complexity.


Key responsibilities include optimizing performance for analytics workloads and enforcing governance, permissions, and security policies. Strong proficiency in SQL and Python is essential, along with experience in modern data stacks such as Snowflake, ClickHouse, and event-streaming technologies. Familiarity with dashboarding tools like Metabase or Tableau is a plus.


This is an opportunity to make a significant impact in a fast-paced, AI-driven environment with a competitive salary, equity, and relocation support.


INTERVIEW PROCESS


  1. CV review and introductory conversation
  2. Technical interview – deep technical discussion
  3. Product interview – communication and collaboration with ML teams


Apply today and be part of shaping the future of AI safety.

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 to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.