Data Architect/Security

Snc-Lavalin
Edinburgh
4 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Principal Data Architect DV Cleared

Data Architect

Data Architect - up to £90,000 + Bonus + Benefits

Data Architect

Data Architect

Data Architect/Security page is loaded## Data Architect/Securitylocations: GB.United Kingdom: CHE.Baden 5400.Nordhaus 3time type: Full timeposted on: Posted 2 Days Agojob requisition id: R-141464### Job DescriptionJob Title: Data Architect / SecurityLocation: UK or Switzerland****Join Us!!****Lead enterprise-wide data architecture strategy and governance:• Define and manage data architecture across SAP S/4HANA, Workday, and cloud environments.• Develop conceptual, logical, and physical data models aligned with business and regulatory requirements.• Establish data governance frameworks, metadata standards, and MDM processes.• Embed governance checkpoints within PMO methodologies and quality gates.• Lead data lifecycle management initiatives ensuring accuracy and traceability.Architect secure and scalable data integration solutions:• Design end-to-end data flows between legacy systems, S/4HANA, and cloud platforms (Azure, AWS, GCP).• Oversee data migration strategies ensuring quality, validation, and audit readiness.• Collaborate with SAP BTP and integration teams to optimise secure interfaces.• Support analytics enablement across SAC, Power BI, and advanced analytics platforms.• Evaluate emerging technologies (e.g., data mesh, AI/ML) to enhance architecture.Champion data security, privacy, and compliance:• Implement data classification, encryption, and access control standards.• Ensure compliance with GDPR, ISO 27001, NIST, SOC 2, and sector-specific frameworks.• Develop secure data integration architectures and monitor sensitive data flows.• Lead security assessments and contribute to incident response planning.• Promote a security-by-design culture across transformation programmes.Enable analytics and sustainability reporting:• Design hybrid data platforms integrating structured and unstructured sources.• Define reference architectures for data lakes, warehouses, and ESG reporting.• Enable self-service analytics through governed data catalogues and metadata repositories.• Align architecture with business analytics, sustainability metrics, and regulatory reporting.Lead cross-functional collaboration and vendor management:• Lead teams of data engineers, modellers, and security analysts.• Liaise between PMO, Enterprise Architecture, Information Security, and business stakeholders.• Provide architectural oversight and assurance across programmes.• Represent data and security interests in governance boards and audits.• Manage vendor relationships, performance, and contracts.Experience Required:• 8–10 years’ experience in data architecture, security architecture, or enterprise transformation roles.• Proven experience in SAP S/4HANA, cloud migration, and data governance programme delivery.• Strong expertise in data modelling, information security, and cloud platforms.• Familiarity with IAM, encryption, DLP technologies, and advanced analytics platforms.• Professional certifications preferred: TOGAF, DAMA DMBOK, CDMP, Azure/AWS Data Architect, SAP Architect, CISSP, CISM.**What we offer:**In return, we offer a wide range of rewards and benefits:• A strategic role with high visibility and impact.• Competitive compensation and performance-based incentives.• Hybrid working model with flexibility and collaboration.• Opportunities for professional development and certification support.• Inclusive and dynamic work environment committed to sustainability and innovation.About Linxon:“Building the infrastructure to power the world”At Linxon, we are leaders in delivering innovative EPC substation projects essential for the energy transition. With expertise from Hitachi Energy and AtkinsRéalis, we provide integrated, sustainable solutions that power cities, industries, and communities. We are committed to building infrastructure that supports a carbon-free future and ensures reliable and efficient energy transmission.By joining Linxon, you will contribute to critical infrastructure projects, driving the transition to a carbon-free energy future. We value diversity and inclusion, offering a supportive and dynamic work environment where every employee can thrive.**Diversity & Inclusion:**We encourage applications from people of all races, ages, genders, religions, sexual orientations, and more—so whoever you are, we hope you’ll see things our way too.### Worker TypeEmployee### Job TypeRegularAt Linxon, we seek to hire individuals with diverse characteristics, backgrounds and perspectives. We strongly believe that world-class talent makes no distinctions based on gender, ethnic or national origin, sexual identity and orientation, age, religion or disability, but enriches itself through these differences.
#J-18808-Ljbffr

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.