At a Glance
- Tasks: Build a data lake and develop pipelines for a single source of truth.
- Company: Innovative tech firm in London with a hybrid work model.
- Benefits: Competitive daily rate, flexible working, and opportunities to use cutting-edge technology.
- Other info: Exciting career growth in a collaborative environment focused on innovation.
- Why this job: Join a dynamic team and shape the future of data engineering with AI tools.
- Qualifications: 2-5 years of data engineering experience, 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.
Ingegnere Dei Dati employer: Norton Blake
Join a forward-thinking company that prioritises 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 supportive environment that values your growth and encourages the use of AI-assisted tools to enhance your engineering skills.
StudySmarter Expert Advice🤫
We think this is how you could land Ingegnere Dei Dati
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the lookout for opportunities. Attend meetups or webinars related to data engineering to meet potential employers and learn about job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data lakes or cloud platforms like Azure and GCP. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and Python skills. Be ready to discuss your experience with ELT/ETL processes and how you've tackled data quality issues in past projects. Practice common interview questions to boost your confidence.
✨Tip Number 4
Don't forget to apply through our website! We have plenty of exciting data engineering roles that could be perfect for you. Keep an eye on our listings and make sure your application stands out by tailoring it to each role.
We think you need these skills to ace Ingegnere Dei Dati
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Data Engineer role. Highlight your hands-on experience with data lakes, SQL, and Python, and don’t forget to mention any cloud platforms you've worked with!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're excited about building the data lake and how your background aligns with our needs. Keep it concise but engaging—show us your personality!
Showcase Your Projects:If you’ve worked on relevant projects, make sure to include them in your application. Whether it's building ingestion pipelines or using AI-assisted tools, we want to see what you've done and how it relates to the role.
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—just a few clicks and you’re done!
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 how you've implemented canonical models in past projects. This will show that you're not just familiar with the theory but have practical knowledge too.
✨Brush Up on SQL and Python
Since solid SQL and Python skills are crucial for this role, take some time to review key concepts and be prepared to solve problems on the spot. You might be asked to write a query or a small script during the interview, so practice makes perfect!
✨Familiarise Yourself with Cloud Platforms
Given the focus on Azure and GCP, ensure you can speak confidently about your experiences with these platforms. Highlight any specific projects where you’ve migrated data or built solutions across different cloud environments, as this will demonstrate your adaptability.
✨Show Enthusiasm for AI Tools
The job mentions using AI-assisted tooling, so express your genuine interest in this area. Share any experiences you've had with AI in data engineering, whether it's schema mapping or data-quality checks, to show that you're forward-thinking and eager to leverage new technologies.
