Full Stack Developer - Python, React - Cork, Hybrid

Cork
9 months ago
Applications closed

Related Jobs

View all jobs

Java Developer with SQL & GIT

Senior Data Engineer

Lead Data Engineer

Senior Data Engineer

Azure Data Engineer

Lead Data Engineer

We are AMS. We are a global total workforce solutions firm; we enable organisations to thrive in an age of constant change by building, re-shaping, and optimising workforces. Our Contingent Workforce Solutions (CWS) is one of our service offerings; we act as an extension of our clients' recruitment team and provide professional interim and temporary resources.

We are currently working with our client, Deloitte Ireland.

At Deloitte, we make an impact that matters for our clients, our people, our profession, and in the wider society by delivering the solutions and insights they need to address their most complex business challenges.

On behalf of Deloitte, AMS are looking for a Full-Stack Developer for an initial 6-month contract on a hybrid basis in Cork.

Purpose of the Role:

Our client Deloitte have won a high profile contract with a world-leading technology powerhouse celebrated for its innovation, scale, and impact.

As a Full Stack Developer, you'll play a pivotal role in building robust, scalable web applications that serve millions of users globally. You'll collaborate with a world-class engineering team and contribute to products that shape industries and elevate user experience.

With a focus on performance, modern architecture, and agile delivery, this role is ideal for developers who are passionate about clean code, ownership, and delivering solutions at scale.

As a Full Stack Developer you will:

Build and maintain front-end applications with modern JavaScript frameworks.

Develop back-end services and APIs using Python and Flask.

Design and manage efficient relational database schemas.

Deploy and manage containerised applications (Kubernetes, Docker).

Support cloud-based infrastructure, ideally within Apple's Internal Cloud.

Collaborate across design, engineering, and product teams.

Write clean, maintainable, and testable code.

What we require from the candidate:

Front-End Development

Experience building responsive user interfaces.

Preferred: React JS.

Acceptable: Vue.js, AngularJS, or comparable frameworks.

Back-End Development

Strong programming skills in Python.

Preferred framework: Flask.

Ability to develop robust, scalable APIs and integrate with front-end components.

Database Management

Proficient with relational databases, ideally MySQL.

Experience with Snowflake (desirable).

Knowledge of schema design, query optimisation, and data integrity best practices.

Containerisation & Cloud Deployment

Experience with Docker and Kubernetes (desirable).

Ability to optimise performance in containerised environments.

Next steps:

If you are interested in applying for this position and meet the criteria outlined above, please click the link to apply and we will contact you with an update in due course.

AMS, a Recruitment Process Outsourcing Company, may in the delivery of some of its services be deemed to operate as an Employment Agency or an Employment Business

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.