Principal Cybersecurity Analytics Data Engineer

HSBC Global Services Limited
Sheffield
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Principal Naval Architect (Weights)

Join a digital first bank that’s powered by people.

Our technology team builds innovative digital solutions rapidly and at scale to deliver the next generation of banking services for our customers around the world.

In our cybersecurity team you’ll be helping to safeguard the financial system on which millions of people depend.

You’ll be making banking more secure by designing, implementing, and operating controls to manage cybersecurity risk. You’ll help define HSBC Group cyber security standards, deliver Global Security Operations ad Threat management services, provide round-the-clock monitoring and security incident response services, and oversee Network/Application/Infrastructure Security. The work you do will provid3e assurance of the adequacy and effectiveness of security controls to Business Risk Owners.

 

The Principal Cybersecurity Analytics Data Engineer, is a key technical role within the Platform & Data Engineering Team, contributing to, coordinating, and leading data engineering, data acquisition, cloud infrastructure and platform engineering, platform operations, and production support activities using ground-breaking cloud and big data technologies. 

 

The position is a senior technical, hands-on delivery role, requiring knowledge of data engineering, cloud infrastructure   and platform engineering, platform operations and production support.

 

In this role you will:

  • Ingest and provision raw datasets, enriched tables, and curated data assets to support various cybersecurity use cases.
  • Drive enhancements to the data ingestion process, with an emphasis on real-time data coverage.
  • Design and implement robust data pipelines that integrate diverse data sources across the enterprise and external platforms.
  • Perform ETL workflows, leveraging both advanced data manipulation tools and custom code, ensuring data is accessible and structured appropriately for all systems and stakeholders.
  • Identify, analyze, and onboard new data sources, conducting exploratory analysis when necessary.

 

To be successful in this role you should meet the following requirements:

  • Strong experience with SRE and Azure DevOps.
  • Proficiency in scripting (Bash/PowerShell, Azure CLI), coding (Python, C#, Java), and querying (SQL, Kusto).
  • Hands-on experience with PowerShell, Terraform, and object-oriented programming languages.
  • Strong experience with cloud & big data technologies, including Azure Cloud, Azure IAM, Azure AD, Azure Data Factory, Databricks, Kubernetes, and PowerBI.
  • Experience with server and infrastructure technologies like Nginx/Apache, CosmosDB, Linux, and tools such as Prometheus, Grafana, and Elasticsearch.

 

This role can be based in both Sheffield and / or Edinburgh

Opening a world of opportunity

 

Being open to different points of view is important for our business and the communities we serve. At HSBC, we’re dedicated to creating diverse and inclusive workplaces - no matter their gender, ethnicity, disability, religion, sexual orientation, or age. We are committed to removing barriers and ensuring careers at HSBC are inclusive and accessible for everyone to be at their best. We take pride in being a Disability Confident Leader and will offer an interview to people with disabilities, long term conditions or neurodivergent candidates who meet the minimum criteria for the role.

 

If you have a need that requires accommodations or changes during the recruitment process, please get in touch with our Recruitment Helpdesk:

Email:

Telephone:

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.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.

Contract vs Permanent Machine Learning Jobs: Which Pays Better in 2025?

Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.