AWS Data Engineer

London
3 months ago
Applications closed

Related Jobs

View all jobs

Lead AWS Data Engineer

Lead AWS Data Engineer

Lead Data Architect | Snowflake & AWS | £130k | Roadmap to Head of Engineering

Databricks SME and AWS Data Engineer

Data Engineer DV Cleared

Senior Data Engineer (AWS, Airflow, Python)

Role: Data Engineer (AWS)
Contract: £450pd-£500pd (Inside IR35)
Location: Remote working with occasional travel to site
Duration: End of March 2026 (expected to extend at the new financial year)

We are currently recruiting for a Data Engineer to work on a project within the Public Sector space. The role will be AWS focused and requires a Data Engineer who can come in and make an impact and difference to the project.

Skills and experience required

Experience of back-end / data engineering across a number of languages (including Python), and commonly used IDE's
Experience with developing, scheduling, maintaining and resolving issues with batch or micro-batch jobs on AWS ETL or Azure ETL services
Experience querying data stored on AWS S3 or Azure ADLSv2, or through a Lakehouse capability
Experience in managing API-level and Database connectivity
Experience using source control and DevOps tooling such as Gitlab
Experience in use of terraform (or similar cloud native products) to build new data & analytics platform capabilities
Experience with developing data features and associated transformation procedures on a modern data platform. Examples include (but not limited to) Azure Fabric, AWS Lakeformation, Databricks or Snowflake.
Experience automating operations tasks with one or more scripting languages.

Due to the nature of the project and the short turnaround required, the successful candidate must hold valid and live SC Clearance.

If you are interested in the role and would like to apply, please click on the link for immediate consideration

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 Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

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

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.