At a Glance
- Tasks: Build a data lake to streamline business data integration and enhance data quality.
- Company: Dynamic tech company focused on innovative data solutions.
- Benefits: Competitive daily rate, hybrid work model, and opportunities for professional growth.
- Other info: Exciting projects using AI tools and cloud technologies.
- Why this job: Join a cutting-edge team and shape the future of data engineering.
- Qualifications: 2-5 years of data engineering experience 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.
Ingegnere Dati employer: Norton Blake
Join a forward-thinking company that prioritises innovation and collaboration, offering a dynamic work culture where your contributions as a Data Engineer will directly impact the creation of a centralised data lake. With competitive daily rates and the flexibility of 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 Dati
✨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 projects, especially those involving data lakes or cloud platforms like Azure and GCP. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and Python skills. Be ready to discuss your hands-on experience with data pipelines and transformations, as well as how you've tackled data quality issues in past projects.
✨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 the attention it deserves.
We think you need these skills to ace Ingegnere 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 a data lake and how your past experiences align with our needs. Keep it engaging and personal – we want to see your passion!
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 love seeing real examples of your work and how you’ve tackled challenges.
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 shows you’re keen to join the StudySmarter team!
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 a few coding challenges during the interview. Practising common data transformation tasks can help you demonstrate your hands-on experience effectively.
✨Familiarise Yourself with Cloud Platforms
Given the focus on Azure and GCP, make sure you know the ins and outs of both platforms. Be ready to discuss any relevant projects you've worked on and how you approached migrating data or building pipelines across different cloud environments.
✨Show Enthusiasm for AI Tools
This role values AI-assisted engineering tooling, so express your genuine interest in how AI can enhance data engineering processes. Share any experiences you've had using such tools and how they improved your workflow or data quality in previous roles.
