Lead Software Engineer - Data

Barclays Bank PLC
Northampton
3 weeks ago
Applications closed

Related Jobs

View all jobs

Lead Software Engineer - Java

Lead Software Developer (Polygot - ASP.Net, C#, Java, Spring)

Lead / Senior Software Engineer - ML/AI

Lead Back-end Engineer

Lead Electronics Engineer

Lead Machine Learning Engineer

Join Barclays as a Senior R&D Software and Data Engineer where you'll spearhead the evolution of our digital landscape, driving innovation and excellence. In this role, you will be an integral part of our Cyber Fraud Fusion Centre, delivering scalable CFFC services to disrupt and prevent upstream economic crime.To be successful as a Senior R&D Software and Data Engineer, you will need the following: ​Experience working within Financial Service teams responsible for cyber fraud, financial crime, or security (web/app).Experience with industry fraud and security signals, including any such as digital identity, device, voice, biometrics, and behavioural profiling technologies.  ​Knowledge of malicious attack vectors used by cyber fraud adversaries to target the financial sector including but not limited to Device Spoofing, Location Manipulation, Identity Fraud, Account Takeover and False documentation.Python, PHP, JavaScript, Java, Relational databases (Postgres, MS SQL, Oracle, MySQL, etc.), SAS PROC SQL, Hue Database Assistant, Teradata, and non-rational Hadoop. ​​Experience working within Financial Service teams responsible for cyber fraud, financial crime, or security (web/app). Advanced knowledge of malicious attack vectors used by cyber fraud adversaries.Knowledge of Enterprise security frameworks such as NIST Cybersecurity Framework and Cyber-attack phases. ​Previous advanced experience using analytical tools and platforms such as SQL/SAS/Hue/Hive Basic, Quantexa, Elastic Search, SAS and MI tools like Tableau and Power BI.Technical experience or advanced knowledge of computing, computer science and networks.​You may be assessed on the 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.To 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.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.Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.Take ownership for managing risk and strengthening controls in relation to the work done.Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience.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.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

AI Jobs for Non‑Technical Professionals: Where Do You Fit In?

Your Seat at the AI Table Artificial Intelligence (AI) has left the lab and entered boardrooms, high‑street banks, hospitals and marketing agencies across the United Kingdom. Yet a stubborn myth lingers: “AI careers are only for coders and PhDs.” If you can’t write TensorFlow, surely you have no place in the conversation—right? Wrong. According to PwC’s UK AI Jobs Barometer 2024, vacancies mentioning AI rose 61 % year‑on‑year, but only 35 % of those adverts required advanced programming skills (pwc.co.uk). The Department for Culture, Media & Sport (DCMS) likewise reports that Britain’s fastest‑growing AI employers are “actively recruiting non‑technical talent to scale responsibly” (gov.uk). Put simply, the nation needs communicators, strategists, ethicists, marketers and project leaders every bit as urgently as it needs machine‑learning engineers. This 2,500‑word guide shows where you fit in—and how to land an AI role without touching a line of Python.

ElevenLabs AI Jobs in 2025: Your Complete UK Guide to Crafting Human‑Level Voice Technology

"Make any voice sound infinitely human." That tagline catapulted ElevenLabs from hack‑day prototype to unicorn‑status voice‑AI platform in under three years. The London‑ and New York‑based start‑up’s text‑to‑speech, dubbing and voice‑cloning APIs now serve publishers, film studios, ed‑tech giants and accessibility apps across 45 languages. After an $80 m Series B round in January 2024—which pushed valuation above $1 bn—ElevenLabs is scaling fast, doubling revenue every quarter and hiring aggressively. If you’re an ML engineer who dreams in spectrograms, an audio‑DSP wizard or a product storyteller who can translate jargon into creative workflows, this guide explains how to land an ElevenLabs AI job in 2025.

AI vs. Data Science vs. Machine Learning Jobs: Which Path Should You Choose?

In recent years, the fields of Artificial Intelligence (AI), Data Science, and Machine Learning (ML) have experienced explosive growth. Spurred by the increase in data availability, advances in computing power, and the demand for intelligent decision-making, organisations of all sizes are investing heavily in these areas. If you’ve been exploring AI jobs on www.artificialintelligencejobs.co.uk, you’ve likely noticed that employers use terms like “AI,” “Data Science,” and “Machine Learning”—often interchangeably. While they are closely related, there are nuanced differences between these fields. Understanding these distinctions is key if you’re trying to decide which path suits you best. This comprehensive guide will help you differentiate among AI, Data Science, and Machine Learning. We will discuss the key skills for each, typical job roles, salary ranges, and provide real-world examples of professionals working in these fields. By the end, you should have a clearer idea of where your strengths and passions might fit, helping you take the next step towards securing your ideal role in the world of data-driven innovation.