AWS Data Engineer

Datatech Analytics
London
9 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer

AWS Data Engineer

AWS Data Engineer Telecom Domain

AWS Cloud Data Engineer

Lead AWS Data Engineer

Lead AWS Data Engineer

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:www.datatech.org.uk

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.