Principal Security Data Analyst

Oracle
Leeds
11 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Sc cleared

Principal Data Analyst

Contract Principal Data Analyst - Hybrid - Reading

Principal Data Engineer (GCP)

Principal Data Engineer (GCP)

Principal Data Engineer

Oracle’s Software Assurance organization has the mission is to make application security and software assurance, at scale, a reality. We are a diverse and inclusive team of architects, researchers, and engineers, combining our unique perspectives and expertise to create secure and innovative solutions to complex challenges. With the resources of a large enterprise and the agility of a start-up, we are working on a greenfield software assurance project.


Work You’ll Do

We are seeking a Security Data Analyst to join our team. This role will combine data analysis, security research, and development skills where you will be responsible for designing, developing a platform capable of analyzing large datasets for security and compliance requirements. You will leverage your expertise in cybersecurity to proactively identify and address emerging threats, ensuring that secure coding practices are seamlessly integrated into every stage of development.


What You’ll Bring

  • Bachelor’s degree in computer science, Engineering, or a related field (or equivalent work experience).
  • 5+ years of experience in software/platform development/engineering from front end (web), mobile, back end, ad tech, or analytics dataflows backgrounds.
  • Extensive experience in dataflows, or similar roles in data management with proven experience building automated and scalable platforms for data-intensive applications.
  • Experience with navigating and handling large data sets and the ability to design and implement scalable and maintainable systems
  • Strong background in API development and associated architectural patterns such as REST or gRPC
  • Programming experience in Python, Go, Java, or similar.
  • Experience with data science concepts such as data preparation, exploration, modelling and the ability to apply this process when handling structured or unstructured data
  • Confident with using common data science tooling such as Jupyter notebooks, pandas, matplotlib, seaborn, numpy
  • API testing and security tools: Postman, Burp Suite, OWASP ZAP, etc.
  • Strong knowledge of database management systems (DBMS) such as MySQL
  • Hands-on experience with security and compliance frameworks and standards.
  • Knowledge of performance optimization techniques for mobile applications, including memory, CPU and network efficiency.
  • Excellent problem-solving and analytical skills.
  • Strong collaboration and communication skills, with the ability to work in cross functional teams and explain complex technical concepts to non-technical stakeholders.


Nice to Have:

  • Experience with OCI cloud-based services
  • Experience with machine learning or AI in security applications.
  • Experience in Agile methodologies and using project management tools like JIRA and confluence.
  • Knowledge of Software Assurance programs

Career Level - IC5


Responsibilities:

  • Architect and develop a secure, high-performance platform to ingest, parse, and analyze large volumes of API data stored in a MySQL database.
  • Work closely with internal and client teams to analyze, define and implement data rules and data flows, translating these into an auditable tool.
  • Scope and execute threat analysis to research, evaluate, track, and manage information security threats and vulnerabilities in data flows.
  • Ensure the tooling is secure by collaborating with architects and security teams to implement best practices for compliance, data privacy, and protection, while integrating tools and frameworks to assess APIs against OWASP and other relevant security standards (NIST, ISO-27001, PCI-DSS, HIPAA, FedRAMP)
  • Automate security and compliance controls into the platform for continuous monitoring and reporting.
  • Execute MySQL queries to ensure data integrity and consistency
  • Create intuitive dashboards and reports for stakeholders.
  • Create tools to help engineering teams identify security-related weaknesses
  • Stay up to date with the latest trends and technologies, contributing to ongoing improvements of platform architecture and best practices.
  • Maintain clear, comprehensive documentation on the platform architecture, services, and technical decisions to support internal teams and future development.
  • Mentor junior engineers and provide technical guidance.

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 Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.