Principal Data Engineer

Oliver Bernard
London
9 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Architect DV Cleared

Data Analyst - Aerospace

Data Analyst - Sc cleared

Data Warehouse Engineer - 6 Month FTC

Principal, AI Data Science

Data Engineer - London based Start-Up
Hybrid working in Central London
Pays £100k-£120k

Data Engineer - Python, SQL, ETL Pipelines, AWS/GCP

Oliver Bernard have partnered with Central London based Start-Up company who are looking to expand their Data function. You'll be joining a small engineering team, who are looking to make a huge impact within the Telecoms industry.

They key focus for the role is building, scaling and maintaining their AWS & GCP data infrastructure, whilst being highly proficient with modern Data Warehousing and scaling ETL processes.

The ideal candidate will be comfortable working in a start-up environment, collaborating with a variety of stakeholders, both technical and non-technical, where strong communication and interpersonal skills are essential to succeed in this role.

Data Engineer - Python, SQL, ETL Pipelines, AWS/GCP

Key skills and requirements:

Demonstrated experience as a Data Engineer scaling Data heavy platforms
Strong understanding of ETL pipelines and Data Architectures
Python, SQL & Python libraries (Pandas & Spark)
Modern Data Warehousing tools
AWS/GCP experience
Experience working in high-growth start-up or scale-up environments (essential)

Hybrid working in Central London
Pays £100k-£120k
Unfortunately, visa sponsorship is unavailable and you must be UK based to be considered

Data Engineer - Python, SQL, ETL Pipelines, AWS/GCP

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.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.