Senior AWS Data Engineer

London
7 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer (contract)

Head of Business Intelligence & Data Analytics

2x Senior Data Engineer (Financial Services)

Senior Data Engineer (Python/PySpark & SQL)

Senior Data Engineer

Senior Data Engineer

Make your mark in a collaborative and purpose-driven team.**

We are seeking a Senior AWS Data Engineer to join a leading organisation's AWS - Data team. This permanent, hybrid position requires you to work in the office two-three days a week in London. This is a great opportunity for candidates who are passionate about data engineering and want to contribute to impactful projects in a supportive environment.

Key Responsibilities:

  • Develop and maintain AWS data pipelines and infrastructure.
  • Collaborate with cross-functional teams to design data solutions.
  • Optimise existing data processes for efficiency and performance.
  • Ensure data quality and security standards are met.
  • Stay up-to-date with AWS developments and best practices.

    Key Requirements:
  • Proven experience with AWS services and tools.
  • Strong knowledge of data modeling and ETL processes.
  • Proficiency in programming languages such as Python or SQL.
  • Excellent problem-solving skills with a proactive approach.
  • Ability to communicate effectively within a team.

    If you are a skilled and driven AWS Data Engineer looking to make an impact, we encourage you to apply for this exciting opportunity

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.