Data Architect (Senior/Principal Level) - Hybrid

London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Company description
Our client teams operate globally from offices in the UK, Ireland, US, Nordics, and Netherlands. With diverse teams of experts combine innovative thinking and breakthrough technologies to progress further, and faster. Their clients adapt and transform, and together they achieve enduring results.
Working with clients in consumer and manufacturing, defence and security, energy and utilities, financial services, government and public services, health and life sciences, and transport. The Data Architect will join the business at a period of huge growth.
Key Responsibilities:

  • Design and Develop Data Architecture: Create, optimise, and maintain conceptual, logical, and physical data models to support the enterprise data strategy.
  • Data Strategy and Governance: Define and implement data management strategies, including data governance, metadata management, and data quality controls.
  • Database and Cloud Technologies: Select appropriate database solutions (SQL, NoSQL, Data Lakes) and cloud platforms (AWS, Azure, Google Cloud) to support the organisation’s data infrastructure.
  • Data Integration: Develop and manage ETL (Extract, Transform, Load) processes to ensure data from multiple sources is properly integrated into centralized systems.
  • Collaboration and Communication: Work closely with business stakeholders, data analysts, data engineers, and clients to understand requirements and deliver scalable data solutions.
  • Security and Compliance: Ensure data security, privacy, and compliance with relevant regulations (e.g., GDPR, HIPAA) by implementing data encryption and anonymisation techniques.
  • Documentation: Creation of detailed documentation of data architecture, flows, and processes for ongoing improvement and knowledge sharing.
    Required Qualifications:
    Experience:
  • 5+ years of experience in data architecture, database design, or data engineering roles.
  • Proven experience with database management systems (e.g., Oracle, SQL Server, PostgreSQL) and data warehousing technologies.
  • Experience with cloud-based data solutions (AWS, Azure, GCP).
  • Familiarity with big data technologies like Hadoop, Spark, and Kafka.
    Technical Skills:
  • Proficiency in data modelling (ERD, normalization) and data warehousing concepts.
  • Strong understanding of ETL frameworks and tools (e.g., Talend, Informatica, Apache NiFi).
  • Knowledge of programming languages such as SQL, Python, or Java.
  • Experience with BI tools (e.g., Power BI, Tableau) and data visualisation best practices.
    Soft Skills:
  • Excellent problem-solving skills and attention to detail.
  • Strong communication skills to explain technical concepts to non-technical stakeholders.
  • Ability to work in a fast-paced, collaborative environment.
    Preferred Qualifications:
  • Familiarity with Agile/Scrum methodologies.
  • Certifications in data management (e.g., CDMP, AWS Certified Data Analytics, Google Cloud Data Engineer).
    Additional information
    Check out some of the extensive benefits:
    • Health and lifestyle perks accompanying private healthcare for you and your family
    • 25 days annual leave (plus a bonus half day on Christmas Eve) with the opportunity to buy 5 additional days
    • Generous company pension scheme
    • Opportunity to get involved with community and charity-based initiatives
    • Annual performance-based bonus
    • Company share ownership
    • Tax efficient benefits (cycle to work, give as you earn)

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.

Jobs Careers

10 Essential Books to Read to Nail Your Data Science Career in the UK

Data science continues to be one of the most exciting and rapidly evolving fields in tech. With industries across the UK—ranging from finance and healthcare to e-commerce and government—embracing data-driven decision-making, the demand for skilled data scientists has soared. Whether you're a recent graduate looking for your first role or a professional aiming to advance your career, staying updated through books is crucial. In this article, we explore ten essential books every data science job seeker in the UK should read. Each book provides valuable insights into core concepts, practical applications, and industry-standard tools, helping you build skills employers are actively looking for.

Careers

Navigating Data Science Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Data science has taken centre stage in the modern workplace. Organisations rely on data-driven insights to shape everything from product innovation and customer experience to operational efficiency and strategic planning. As a result, there is a growing need for skilled data scientists who can analyse large volumes of data, build predictive models, communicate findings effectively, and collaborate cross-functionally. If you are looking to accelerate your data science career—or even land your first role—attending data science career fairs can be a game-changer. Unlike traditional online applications, face-to-face interactions let you showcase your personality, passion, and communication skills in addition to your technical expertise. However, to stand out in a busy environment, you need a clear strategy: from polishing your personal pitch and asking thoughtful questions to following up with a memorable message. In this article, we’ll guide you through every step of making a strong impression at data science career fairs in the UK and beyond.

Jobs

Common Pitfalls Data Science Job Seekers Face and How to Avoid Them

Data science has become a linchpin for decision-making and innovation across countless industries, from finance and healthcare to tech and retail. The demand for data scientists in the UK continues to climb, with businesses seeking professionals who can interpret complex datasets, build predictive models, and communicate actionable insights. Despite this high demand, the job market can be extremely competitive—and many applicants unknowingly fall into avoidable traps. Whether you’re an aspiring data scientist fresh out of university, a professional transitioning from a quantitative role, or a seasoned analyst looking to expand your skill set, it’s crucial to navigate your job search effectively. In this article, we explore the most common pitfalls data science job seekers face and provide pragmatic advice to help you stand out. By refining your CV, portfolio, interview strategies, and communication skills, you can significantly increase your chances of landing a rewarding data science role. If you’re looking for your next data science job in the UK, don’t forget to explore the listings at Data Science Jobs. Read on to discover how to avoid critical mistakes and position yourself for success.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.