Data Engineer

QAD
Nottingham
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer - AI Analytics and EdTech Developments

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

QAD Inc. is a leading provider of adaptive, cloud-based enterprise software and services for global manufacturing companies. Global manufacturers face ever-increasing disruption caused by technology-driven innovation and changing consumer preferences. In order to survive and thrive, manufacturers must be able to innovate and change business models at unprecedented rates of speed. QAD calls these companies Adaptive Manufacturing Enterprises. QAD solutions help customers in the automotive, life sciences, packaging, consumer products, food and beverage, high tech and industrial manufacturing industries rapidly adapt to change and innovate for competitive advantage.

We are looking for talented individuals who want to join us on our mission to help solve relevant real-world problems in manufacturing and the supply chain.


This role is fully remote in UK, with full work authorization already in effect. No Visa sponsorship is available.


Job Description


In a data-driven and AI-oriented environment, you will be responsible for the design, industrialization, and optimization of inter-application data pipelines. You will be involved in the entire data chain, from data ingestion to its use by data science teams and AI systems in production within a human-sized and multidisciplinary team. This role is within Process Intelligence (PI) team that combines functions such as Process Mining, Real Time Monitoring and Predictive AI


Key responsibilities:

  • Design and maintain scalable data pipelines.
  • Structure, transform, and optimize data in Snowflake.
  • Implement multi-source ETL/ELT flows (ERP, APIs, files).
  • Leverage the AWS environment, including S3, IAM, and various data services.
  • Prepare data for Data Science teams and integrate AI/ML models into production.
  • Ensure data quality, security, and governance.
  • Provide input on data architecture.


Qualifications

  • 5+ years of experience in data engineering, including significant experience in a cloud environment.
  • Snowflake (MUST HAVE): Expertise in modeling, query optimization, cost management, and security.
  • AWS: Strong knowledge of data and cloud services including S3, IAM, Glue, and Lambda.
  • Languages: Advanced SQL and Python for data manipulation, automation, and ML integration.
  • Data Engineering: Proven experience in ETL/ELT pipeline design.
  • AI/ML Integration: Ability to prepare data for model training and deploy AI models into production workflows (batch or real-time).


Nice to Have:

  • Experience with agentic AI architectures, including agent orchestration and decision loops.
  • Integration of agent-driven AI models into existing data pipelines.
  • Knowledge of modern architectures such as Lakehouse or Data Mesh.

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 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.

Jobs

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.

Education

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.

🍪 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.