At a Glance
- Tasks: Build a data lake to streamline business data integration and enhance data quality.
- Company: Innovative tech firm focused on data solutions in a hybrid work environment.
- Benefits: Competitive daily rate, flexible working options, and opportunities for professional growth.
- Other info: Dynamic role with a focus on collaboration and innovation in data engineering.
- Why this job: Join a cutting-edge project that leverages AI and cloud technology to transform data management.
- 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
As a Data Engineer at our London-based company, you'll be part of a dynamic team dedicated to building a cutting-edge data lake that serves as the backbone of our business intelligence. We pride ourselves on fostering a collaborative work culture that encourages innovation and professional growth, offering competitive daily rates and the flexibility of a hybrid working model. Join us to leverage your skills in a supportive environment where your contributions directly impact our data strategy and success.
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 and sets 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 hands-on experience with data transformations and how you've tackled challenges in previous roles. Practice makes perfect!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your approach to highlight your experience with AI-assisted tooling and cloud platforms, and let’s get you that interview!
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 on!
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 for the best chance of getting noticed. It’s super easy, and you’ll be one step closer to joining our team at StudySmarter!
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 Purpose
Familiarise yourself with the concept of a data lake and its importance as a single source of truth. Be prepared to explain how you would approach building out the data lake and implementing the canonical model. This shows that you understand the bigger picture and can align with the company's goals.
✨Showcase Your Problem-Solving Skills
Think of specific challenges you've faced in previous roles related to data quality and reliability. Be ready to discuss how you implemented validation, monitoring, and alerting in your pipelines. This will demonstrate your ability to keep systems dependable and your proactive approach to problem-solving.
✨Emphasise Your Adaptability
Since the role involves working across different cloud platforms, highlight your experience with both Azure and GCP. Discuss any projects where you had to adapt to new technologies or methodologies, especially if you used AI-assisted tooling. This will show that you're flexible and eager to learn.
