At a Glance
- Tasks: Build a data lake to streamline business data integration and enhance data quality.
- Company: Dynamic tech firm focused on innovative data solutions.
- Benefits: Competitive daily rate, hybrid work model, and opportunities for skill development.
- Other info: Collaborative environment with a focus on AI-assisted engineering tools.
- Why this job: Join a cutting-edge project that shapes the future of data management.
- Qualifications: 2-5 years in data engineering with strong SQL and Python skills.
A hands-on engineer to build out the data lake that will become the single source of truth for the business — so that iPaaS pulls from one governed, canonically-modelled platform rather than integrating directly with five separate systems. Working to the architecture and standards set by the Data Lead, this role delivers the pipelines, models and integrations that make the lake real. Day to day on Microsoft (Azure / Fabric) today, with a likely move to Google Cloud, so portable, vendor-neutral build habits matter.
What You'll Do
- Build the lake: Develop ingestion pipelines and the landing → curated → serving layers, following the platform design and patterns set by the Data Lead.
- Implement the canonical model: Map and transform data from the five source systems into the shared canonical model, so downstream consumers work from one consistent vocabulary.
- Re-point iPaaS: Migrate integrations to source from the lake, building reusable ingestion/publishing flows and helping retire legacy point-to-point connections.
- Data quality & reliability: Implement validation, monitoring and alerting; keep pipelines tested, documented and dependable.
- Use AI in the build: Apply AI-assisted tooling — schema mapping, data-quality checks, code and pipeline generation — to work faster, and help prepare clean, well-structured data for AI/ML and analytics consumption.
- Build portably: Use open table formats (Delta / Iceberg), SQL, Python and infrastructure-as-code so the Azure→GCP move is straightforward.
What You'll Bring
- 2–5 years of hands-on data engineering, ideally including work on a data lake or lakehouse.
- Solid SQL and Python, with practical ELT/ETL experience (event streaming, CDC or API-led integration a plus).
- Comfortable building data transformations to a defined model; exposure to canonical / dimensional modelling.
- Hands-on with a cloud data platform — Azure / Fabric and/or GCP (BigQuery, Dataflow); willing to work across both.
- Experience with, or genuine enthusiasm for, AI-assisted engineering tooling.
- Works well to someone else's architecture and standards, asks good questions, and documents as they go.
IT Data Architect employer: Norton Blake
Join a forward-thinking company that values innovation and collaboration, offering a dynamic work culture where your contributions directly impact the creation of a centralised data lake. With competitive daily rates and a hybrid working model in London, you'll have access to continuous professional development opportunities and the chance to work with cutting-edge technologies like Azure and Google Cloud. Embrace a role that not only challenges you but also supports your growth in a rapidly evolving field, making it an ideal environment for passionate IT Data Architects.
StudySmarter Expert Advice🤫
We think this is how you could land IT Data Architect
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving data lakes or cloud platforms. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and Python skills. Be ready to discuss your experience with data transformations and how you've tackled challenges in past projects.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us. Plus, it makes tracking your application easier for both you and us.
We think you need these skills to ace IT Data Architect
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the IT Data Architect role. Highlight your hands-on data engineering experience, especially with data lakes and cloud platforms like Azure and GCP.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're the perfect fit for this role. Share specific examples of how you've built data pipelines or worked with canonical models, and don’t forget to show your enthusiasm for AI-assisted tooling!
Showcase Your Technical Skills:Be sure to list your technical skills prominently, especially SQL and Python. If you have experience with ELT/ETL processes or cloud data platforms, make it clear! We want to see what you can bring to the table.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Norton Blake
✨Know Your Data Lakes
Make sure you understand the concept of data lakes and how they function. Be ready to discuss your experience with building ingestion pipelines and transforming data, as this role is all about creating a single source of truth for the business.
✨Brush Up on SQL and Python
Since solid SQL and Python skills are crucial for this position, take some time to review key concepts and be prepared to demonstrate your knowledge. You might even want to practice writing some queries or scripts that could come up during the interview.
✨Familiarise Yourself with Cloud Platforms
This role involves working with Azure and potentially Google Cloud, so it’s essential to know the basics of both platforms. If you have experience with infrastructure-as-code, be ready to share examples of how you've used it in past projects.
✨Show Enthusiasm for AI Tools
The job mentions using AI-assisted tooling, so express your genuine interest in this area. Share any relevant experiences where you've applied AI tools in your work, and be open to discussing how you think they can enhance data engineering processes.
