At a Glance
- Tasks: Build a data lake and develop pipelines for seamless data integration.
- Company: Dynamic tech company focused on innovative data solutions.
- Benefits: Competitive daily rate, remote work flexibility, and opportunities for skill development.
- Other info: Exciting projects with potential for career advancement in a collaborative environment.
- Why this job: Join a cutting-edge team and shape the future of data engineering.
- 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.
Data Engineer Con Spark/Scala (Remoto) in London employer: Norton Blake
Join a forward-thinking company that values 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 encourages growth and the use of AI-assisted tools to enhance your engineering skills.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer Con Spark/Scala (Remoto) in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with Azure or GCP. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data lake projects, pipelines, and any AI-assisted tools you've used. This will give potential employers a taste of what you can bring to the table.
✨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.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it makes it easier for us to keep track of your application.
We think you need these skills to ace Data Engineer Con Spark/Scala (Remoto) in London
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 the role and how your background aligns with our needs. Be sure to mention your enthusiasm for AI-assisted engineering tooling and your adaptability to different cloud environments.
Showcase Your Projects:If you’ve worked on relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. Describe your role in building data pipelines or working with canonical models, as this will demonstrate your practical experience.
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 on joining the StudySmarter team!
How to prepare for a job interview at Norton Blake
✨Know Your Data Engineering Basics
Make sure you brush up on your data engineering fundamentals, especially around data lakes and ETL processes. Be ready to discuss your hands-on experience with SQL and Python, as well as any projects you've worked on that involved building ingestion pipelines or data transformations.
✨Familiarise Yourself with the Tech Stack
Since the role involves working with Azure and potentially Google Cloud, it’s crucial to understand these platforms. Get comfortable with their features and how they relate to data engineering. If you’ve used AI-assisted tooling, be prepared to share specific examples of how it improved your workflow.
✨Prepare for Scenario-Based Questions
Expect questions that ask you to solve hypothetical problems related to data quality and reliability. Think about how you would implement validation and monitoring in your pipelines. Practising these scenarios can help you articulate your thought process clearly during the interview.
✨Show Enthusiasm for Collaboration
This role requires working closely with a Data Lead and adhering to established architecture and standards. Be ready to discuss how you’ve collaborated in past projects, asked insightful questions, and documented your work. Highlighting your teamwork skills will show you’re a good fit for their culture.
