Data Architect

Leonardo
Edinburgh
2 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Leonardo Edinburgh, Scotland, United Kingdom


Data Architect

Join to apply for the Data Architect role at Leonardo


The Data Architect is responsible for designing, creating, deploying, and managing an organization’s data architecture. This role ensures that data across systems is accurate, accessible, secure, and supports business objectives.


As a Data Architect, you will have extensive experience in the development of data‑intensive systems. You should have worked with customers to derive requirements and design solutions to meet their needs. The successful applicant will have at least 5+ years of experience and have seen a project through from inception to delivery.


To succeed in this position you will need to be proactive and tirelessly collaborate with IT teams, data engineers, system engineers, and stakeholders to define data models, data flow processes, and governance frameworks.


What You’ll Do

  • Design and implement enterprise‑level data architecture solutions, including databases, data warehouses, and data lakes.
  • Develop and maintain logical, physical, and conceptual data models.
  • Define data management standards, policies, and best practices.
  • Work with data engineers to design and optimise ETL/ELT pipelines for structured and unstructured data.
  • Ensure data quality, consistency, and security across platforms.
  • Collaborate with business stakeholders to understand data requirements and translate them into technical solutions.
  • Evaluate and recommend new data technologies, tools, and platforms to enhance data capabilities.
  • Oversee data integration across cloud and on‑premises environments.
  • Support data governance initiatives, including metadata management, master data management (MDM), and compliance.
  • Provide technical leadership and mentorship to data engineering and analytics teams.

What You’ll Bring

Successful candidates will have previous experience as a data architect or data/software engineer in a similar role. Attributes required include:


Required Qualifications

  • Bachelor’s degree in STEM, Computer Science, Information Systems, Data Science, or related field (Master’s preferred).
  • Strong experience in data architecture, database design, or data engineering.
  • Strong proficiency in SQL and database technologies (e.g., Oracle, SQL Server, PostgreSQL, MySQL).
  • Knowledge of data warehouse and lakehouse architectures.
  • Familiarity with ETL/ELT and orchestration tools (e.g., Informatica, Talend, Apache Airflow).
  • Experience establishing and maintaining data governance frameworks.
  • Experience complying with data security requirements.
  • Experience designing data models.
  • Excellent problem‑solving, communication, and documentation skills.
  • Familiarity with AI/ML data pipelines and analytics platforms.

Preferred Qualifications

  • Experience with cloud data platforms (e.g., AWS, Azure, Google Cloud).
  • Experience with big data technologies (e.g., Hadoop, Spark, Kafka).
  • Experience with UML/SYSML.
  • Strong understanding of API integration and microservices data flow.

This is not an exhaustive list, and we are keen to hear from you even if you might not have experience in all the above. The most important skill is a good attitude and willingness to learn.


Security Clearance

This role is subject to pre‑employment screening in line with the UK Government’s Baseline Personnel Security Standard (BPSS). All successful applicants must be eligible for full security clearance and access to UK‑caveated and ITAR controlled information. For more information and guidance, please visit https://www.gov.uk/government/publications/united-kingdom-security-vetting-clearance-levels


Why Join Us

  • Time to Recharge – Enjoy generous leave with the opportunity to accrue up to 12 additional flexi‑days each year.
  • Secure your Future – Benefit from our award‑winning pension scheme with up to 15% employer contribution.
  • Your Wellbeing Matters – Free access to mental health support, financial advice, and employee‑led networks championing inclusion and diversity (Enable, Pride, Equalise, Armed Forces, Carers, Wellbeing and Ethnicity).
  • Rewarding Performance – All employees at management level and below are eligible for our bonus scheme.
  • Never Stop Learning – Free access to 4,000+ online courses via Coursera and LinkedIn Learning.
  • Refer a friend – Receive a financial reward through our referral programme.
  • Tailored Perks – Spend up to £500 annually on flexible benefits including private healthcare, dental, family cover, tech & lifestyle discounts, gym memberships and more.
  • Flexible working – Flexible hours with hybrid working options. For part‑time opportunities, please talk to us about what might be possible for this role.

Additional Details

  • Primary Location: GB – Edinburgh
  • Additional Locations: GB – Newcastle
  • Contract Type: Permanent
  • Hybrid Working: Hybrid
  • Seniority level: Mid‑Senior level
  • Employment type: Full‑time
  • Job function: Engineering and Information Technology
  • Industries: Defense and Space Manufacturing, Aviation and Aerospace Component Manufacturing, Computer and Network Security


#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.