Data Engineer

Barclays Bank PLC
Glasgow
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer - AI Analytics and EdTech Developments

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - Python, SQL, Machine Learning - Robotics

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - DataOps

Join us to build the next-gen Advanced Data and Analytics Platform. We are looking for a dedicated and experienced Data Engineer with very good ETL/ELT and Data Warehousing skills, to work in a collaborative and friendly team on exciting automation deliveries, on premise and in cloud. As a Data Engineer, you’ll be responsible for the build and delivery of the Ops Analytics Platform across a number of hosting platforms building robust, scalable data pipelines. You will be automating the deployment and maintenance of the Advanced Analytics Platform using the latest and greatest technologies and CI/CD tools such as dbt, GitLab etc. You will also deliver self-service capabilities and automations to enable easy adoption of the Ops Advanced Analytics Platform. Key skills required for this role include:Proficiency in Python and SQL is critical for data manipulation and building data pipelinesAbility to design and maintain robust ETL/ELT processesStrong experience in managing and optimizing data warehouses (e.g., Snowflake, Redshift)Additional skills include:Expertise in cloud platforms such as AWS, Azure, or GCP for modern data solutionsHands-on experience with big data frameworks like Apache Spark or HadoopExperience with tools for pipeline automation and monitoring (e.g., Airflow, Prometheus)You may be assessed on key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job specific technical skills. The role is based out of our office in Glasgow.Purpose of the roleTo build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure. Accountabilities· Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.· Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.· Development of processing and analysis algorithms fit for the intended data complexity and volumes.· Collaboration with data scientist to build and deploy machine learning models.Analyst Expectations· Will have an impact on the work of related teams within the area.· Partner with other functions and business areas.· Takes responsibility for end results of a team’s operational processing and activities.· Escalate breaches of policies / procedure appropriately.· Take responsibility for embedding new policies/ procedures adopted due to risk mitigation.· Advise and influence decision making within own area of expertise.· Take ownership for managing risk and strengthening controls in relation to the work you own or contribute to. Deliver your work and areas of responsibility in line with relevant rules, regulation and codes of conduct.· Maintain and continually build an understanding of how own sub-function integrates with function, alongside knowledge of the organisations products, services and processes within the function.· Demonstrate understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.· Make evaluative judgements based on the analysis of factual information, paying attention to detail.· Resolve problems by identifying and selecting solutions through the application of acquired technical experience and will be guided by precedents.· Guide and persuade team members and communicate complex / sensitive information.· Act as contact point for stakeholders outside of the immediate function, while building a network of contacts outside team and external to the organisation.All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave

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.