AWS Data Engineer

London
9 months ago
Applications closed

Related Jobs

View all jobs

Junior / Graduate Data Scientist

Junior Data Engineer (Data Science)

Data Scientist, Machine Learning Engineer, Data Analyst, Data Engineer, AI Engineer, Business Intelligence Analyst, Data Architect, Analytics Engineer, Research Data Scientist, Statistician, Quantitative Analyst, ML Ops Engineer, Applied Scientist, Insigh

Data Engineering & Data Science Consultant

Data Engineering & Data Science Consultant

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

AWS Data Engineer
Salary: Negotiable to £80,000 Dependent on Experience
London: Hybrid working 3 days per week in the office 2 days home-based
Job Ref: J12931

A leader in consumer behaviour analytics, seeks a driven AWS Data Engineer to guide data infrastructure architecture, working alongside a small talented team of engineers, analysts, and data scientists. In this role, you'll enhance the data platform, develop advanced data pipelines, and integrate cutting-edge technologies like DataOps and Generative AI, including Large Language Models (LLMs).
You'll have proven experience developing AWS Cloud platforms end to end, orchestrating data using Dagster or similar as well as coding in Python and SQL. This is an exciting opportunity for someone looking to challenge themselves in a collaborative environment, with scope to be instrumental in the scaling of the data infrastructure.

Key Responsibilities
·Develop and optimize ETL/ELT processes to support data transformation and integrity for analytics.
·Explore and evaluate new data warehousing solutions, including Snowflake, to improve data accessibility and scalability.
·Partner with product and engineering teams to define data architecture and best practices for reporting.
·Ensure data security, compliance, and governance across data systems.
·Implement and maintain CI/CD pipelines to automate data workflows and enhance system reliability.
·Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability and performance.

Essential Skills and Experience:
·Hands-on experience with AWS services, including Lambda, Glue, Athena, RDS, and S3.
·Strong SQL skills for data transformation, cleaning, and loading.
·Strong coding experience with Python and Pandas.
·Experience with any flavour of data pipeline and workflow management tools: Dagster, Celery, Airflow, etc.
·Build processes supporting data transformation, data structures, metadata, dependency and workload management.
·Experience supporting and working with cross-functional teams in a dynamic environment.
·Strong communication skills to collaborate with remote teams (US, Canada)

Nice to Have
·Familiarity with LLMs including fine-tuning and RAG.
·Knowledge of Statistics
·Knowledge of DataOps best practices, including CI/CD for data workflows.

Please note we can only accept applications from those with current UK working rights for this role, this client cannot offer visa sponsorship.

If this sounds like the role for you then please apply today!

Alternatively, you can refer a friend or colleague by taking part in our fantastic referral schemes! If you have a friend or colleague who would be interested in this role, please refer them to us. For each relevant candidate that you introduce to us (there is no limit) and we place, you will be entitled to our general gift/voucher scheme.
Datatech is one of the UK's leading recruitment agencies in the field of analytics and host of the critically acclaimed event, Women in Data. For more information, visit our website: (url removed)

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.