At a Glance
- Tasks: Lead the design of enterprise data architecture and create robust data models.
- Company: Dynamic tech firm in Central London with a focus on innovation.
- Benefits: Competitive salary, hybrid work model, and professional development opportunities.
- Other info: Join a collaborative team with excellent career growth potential.
- Why this job: Shape the future of data architecture and make a real impact in a senior role.
- Qualifications: Strong SQL skills and experience with Databricks and cloud data technologies.
The predicted salary is between 72000 - 88000 £ per year.
We are hiring a Data Architect to define, design, and evolve enterprise data architecture across modern cloud and lakehouse platforms. You will translate business, analytics, and AI requirements into robust data models, integration patterns, platform standards and governance controls that create trusted, reusable data assets. This is a senior technical leadership role with architecture ownership scope: you will shape data architecture principles, guide design reviews, collaborate with engineering and analytics teams, and make practical trade-offs across data modelling, integration, governance, performance, security, cost, and operational resilience.
Duties & Responsibilities:
- Lead the design and evolution of enterprise data architecture that supports business intelligence, analytics, automation, and AI use cases.
- Develop conceptual, logical, and physical data models for core business domains, data products, reporting layers and analytical platforms.
- Define data integration patterns, data flow designs, and reusable architecture standards for batch, streaming, API-based, and event-driven data movement.
- Establish data governance, data quality, metadata, lineage, security, and privacy standards in partnership with engineering, governance, and business stakeholders.
- Collaborate with data engineers, analytics engineers, product owners, and business stakeholders to translate requirements into scalable, maintainable, and secure data solutions.
- Provide technical leadership through architecture reviews, design documentation, data standards, reference patterns, and guidance for delivery teams.
- Strong SQL, data modelling, and data platform skills, with practical experience designing schemas, semantic layers, dimensional models and enterprise data structures.
- Hands-on experience with modern data platforms such as Databricks, Microsoft Fabric, Azure Data Lake, Azure Synapse, Snowflake or comparable cloud data technologies.
- Experience designing lakehouse, data warehouse, data mart, and data product architectures that support trusted analytics and operational reporting.
- Ability to define metadata, lineage, master data, reference data, and data quality approaches that improve trust, discoverability, and reuse of data assets.
- Experience using Databricks, Spark, and lakehouse concepts for scalable data storage, transformation, governance, and analytical consumption.
- Familiarity with Microsoft Fabric, Azure Data Factory, Azure Synapse, Azure Purview or comparable cloud-based data architecture and governance services.
- Experience with engineering practices, including Git, automated testing, CI/CD, infrastructure-as-code, documentation, and operational monitoring for data platforms.
- Ability to use AI-assisted development and documentation tools responsibly to improve architecture design, productivity, and knowledge sharing.
- Proven track record delivering data architecture, data platform, or enterprise data management solutions in production environments.
Certifications (Nice to Have):
- Degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Mathematics or equivalent practical experience.
- Relevant Microsoft Azure certifications or exams such as DP-900, DP-203, DP-600, AZ-305 or AI-102.
- Relevant Databricks certifications such as Data Engineer Associate, Data Engineer Professional or Lakehouse Fundamentals.
- Any recognised certifications in data architecture, data governance, cloud architecture, data management or enterprise architecture are advantageous.
Data Architect- Perm role- Central London employer: Careerwise
Careerwise is an excellent employer that fosters a collaborative work culture, offering flexible working arrangements with just two days a week in the vibrant city of London. Employees benefit from continuous professional development opportunities and are encouraged to innovate in their roles, making it a rewarding environment for those passionate about data quality and governance.
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We think this is how you could land Data Architect- Perm role- Central London
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We think you need these skills to ace Data Architect- Perm role- Central London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Careerwise, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Careerwise. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Careerwise
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
