Senior Data Engineer (95% Remote)

Optima Dev
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

You’ll probably be the kind of Engineer who loves rolling up their sleeves, and still getting stuck into coding. You’ll probably like the sound of having no legacy tech to worry about – and you’re probably looking for the chance to actually influence decisions.


Well, this is the role for you.


You’d be joining a team who’ve built their data infrastructure from scratch over the last few years. Even better, they’re having no issues and things are going well – but they don’t want to stand still.


But as they’re continuing to grow, they’re looking to take it to the next level and make their infrastructure more mature – so you’ll come in to help with reliability and stability.


They already have plans to form a Central Data Hub (which you’ll play a big part in) – and establishing a larger data mesh.


Your focus will be on all things data processing within Databricks, ingestion pipelines, and DataOps/DevOps.


Tech wise, you’d surround yourself with PySpark/Python, Azure, Kubernetes, Terraform and IaaC. Of course, you’ll ideally have exposure with most of it.


As it stands, their Data team is only small – just one other Data Engineer at the mo. So you’ll get the chance to put your own stamp on things, and take ownership of your own work.


Salary wise, they’ll pay anywhere from £70,000-£82,000 DOE. It’s majority remote – heading into Oxford once every couple of months or so.


They can interview this side of Christmas too.


Get in touch with Jack Leeming @ Optima Dev for a chat.


You need to be UK-based, and they can't offer sponsorship.

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 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.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.