Lead Data Engineer - Hybrid working

Ashdown Group
London, United Kingdom
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Lead

Corecom Consulting York, YO1 8RS, United Kingdom

Data Engineer

Damia Group London, United Kingdom

Senior Data Engineer - Azure, BI & Data Strategy

Consortium Professional Recruitment Hessle, United Kingdom

Data Lead | Energy |to| Bonus

Opus Recruitment Solutions Chiswick, London, W4 5PS, United Kingdom
£95,000 – £110,000 pa Permanent

Senior Data Scientist

Adria Solutions Manchester, United Kingdom

Head of Data and Product

Aspire Personnel Ltd Poole, Dorset, BH13 7EE, United Kingdom
£65,000 – £75,000 pa On-site
Posted
25 Feb 2026 (2 months ago)


Senior Data Engineer (Hands-On / Technical Lead Focus)

Hybrid working - Central London

Ashdown Group are partnering with an innovative, data-led organisation who are recruiting a Senior Data Engineer to take ownership of core data architecture and lead from the front technically.

This is a hands-on senior role for someone who enjoys building robust data systems while mentoring others and raising engineering standards.

The Role

You will design, build, and optimise scalable data pipelines and models across Snowflake, Databricks, and cloud environments (AWS/GCP). Working closely with Data Science, Product, and Analytics teams, youll ensure reliable, high-performance data flows that power AI and analytics products.

Alongside deep technical delivery, youll be responsible for leading the junior engineers and near shore partners as well as contribute to architectural direction and best practices.

Key Responsibilities

  • Design and maintain scalable data architectures across Snowflake and Databricks
  • Lead schema design, dimensional modelling, and SQL performance optimisation
  • Build robust ETL/ELT pipelines
  • Develop resilient API ingestion workflows
  • Implement...

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.