At a Glance
- Tasks: Build a data lake and develop pipelines for seamless data integration.
- Company: Innovative tech firm focused on data solutions in a hybrid work environment.
- Benefits: Competitive daily rate, flexible working, and opportunities to work with cutting-edge technology.
- Other info: Exciting career growth potential in a collaborative and innovative setting.
- 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 required.
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 (f/m) employer: Norton Blake
Join a forward-thinking company that values innovation and collaboration, where as a Data Engineer in London, you'll have the opportunity to shape the future of data management. With a strong emphasis on employee growth, we offer a dynamic work culture that encourages continuous learning and the use of cutting-edge technology, including AI-assisted tools. Enjoy the flexibility of a hybrid work model while contributing to impactful projects that drive the business forward.
StudySmarter Expert Advice🤫
We think this is how you could land Ingegnere dei dati (f/m)
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, attend meetups, and engage in online forums. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving data lakes or cloud platforms. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨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. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for talented data engineers like you. Plus, it’s a great way to ensure your application gets noticed by the right people.
We think you need these skills to ace Ingegnere dei dati (f/m)
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 on!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data engineering and how you can contribute to building our data lake. Be specific about your past projects and how they relate to the role.
Showcase Your Problem-Solving Skills:In your application, give examples of how you've tackled challenges in data engineering. Whether it’s improving data quality or implementing new integrations, we want to see your thought process and solutions.
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status!
How to prepare for a job interview at Norton Blake
✨Know Your Data Engineering Basics
Make sure you brush up on your SQL and Python skills before the interview. Be ready to discuss your hands-on experience with data lakes and ELT/ETL processes, as these are crucial for the role. Prepare examples of how you've built ingestion pipelines or worked with cloud platforms like Azure or GCP.
✨Understand the Role's Requirements
Familiarise yourself with the specific tasks mentioned in the job description, such as building the data lake and implementing the canonical model. Think about how your previous experiences align with these responsibilities and be prepared to share relevant stories during the interview.
✨Showcase Your Problem-Solving Skills
Data engineering often involves troubleshooting and optimising pipelines. Be ready to discuss challenges you've faced in past projects and how you overcame them. This will demonstrate your ability to maintain data quality and reliability, which is key for this position.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's data architecture and the tools they use. This shows your genuine interest in the role and helps you understand how you can contribute effectively. Asking about their approach to AI-assisted tooling could also highlight your enthusiasm for innovative solutions.
