At a Glance
- Tasks: Design and build data pipelines for real-time analytics in a fast-growing digital platform.
- Company: Join a rapidly scaling UK-based consumer platform with a focus on data-driven growth.
- Benefits: Competitive salary, remote work, and clear progression opportunities in a dynamic environment.
- Other info: Collaborate with diverse teams to tackle complex data challenges at scale.
- Why this job: Make a real impact on data engineering and shape the future of analytics.
- Qualifications: Strong experience in data engineering, Python, SQL, and cloud environments.
The predicted salary is between 90000 - 100000 € per year.
This is an opportunity to join a fast scaling, digital first consumer platform at a pivotal point in its data journey. They are investing heavily in their data infrastructure and are building a high performing, software led data engineering team to support real time, high volume analytics across the business. You will have genuine influence over how data engineering is done, working on modern tooling and best practice from day one.
The Company
They are a UK based, consumer facing digital platform operating in a highly regulated online environment. Since launching less than a decade ago, the business has grown rapidly, supported by significant recent funding and a strong focus on product, user experience and responsible engagement. Data is central to their growth strategy, and they are building a modern, scalable analytics platform to support this ambition. The team operates fully remotely across the UK and Europe.
The Role
- Design, build and own end to end data pipelines supporting real time and batch analytics
- Work on high volume, low latency data use cases with a strong focus on reliability and scalability
- Develop production grade data solutions using software engineering best practices such as CI/CD, testing and version control
- Contribute to the design of data models and analytics layers used across the business
- Help implement and mature staging, testing and deployment processes across the data platform
- Collaborate closely with analytics, product and engineering teams to enable better decision making
Your Skills and Experience
- Strong commercial experience as a data engineer in modern, cloud based environments
- Advanced Python and SQL, with experience using PySpark or similar distributed processing frameworks
- Experience building and maintaining data pipelines in AWS using services such as S3, Glue or EKS
- Hands on experience with modern analytics stacks such as Snowflake, dbt and Iceberg
- Familiarity with orchestration tools such as Dagster or Airflow
- Strong understanding of data modelling concepts and analytics best practice
- A software engineering mindset, including Git, testing and CI/CD workflows
What They Offer
- The chance to help shape a growing data platform and engineering culture
- Clear progression opportunities as the data team continues to expand
- Exposure to complex, real world data challenges at scale
How to Apply
If you are a Senior Data Engineer looking to make a real impact in a growing, product led business, apply now to learn more about the opportunity.
(111) Senior Data Engineer in London employer: LinkedIn
Join a dynamic and rapidly growing digital platform that places data at the heart of its strategy, offering you the chance to shape a cutting-edge data engineering culture. With a fully remote work environment across the UK and Europe, you'll enjoy a flexible work-life balance, competitive salary, and clear progression opportunities as the team expands. This is an exciting opportunity to tackle complex data challenges while working with modern tools and best practices in a supportive and innovative atmosphere.
StudySmarter Expert Advice🤫
We think this is how you could land (111) Senior Data Engineer in London
✨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. You never know who might have a lead or can refer you to a hiring manager.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those involving AWS, Python, and SQL. 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 common data engineering challenges and solutions. Be ready to discuss your experience with real-time analytics and how you've tackled scalability issues in past projects.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace (111) Senior Data Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experience mentioned in the job description. Highlight your commercial experience as a data engineer, especially with cloud-based environments and tools like AWS, Python, and SQL.
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 our growing data platform. Mention specific projects or experiences that align with the role's requirements.
Showcase Your Technical Skills:Don’t forget to include any relevant technical skills, especially those related to data pipelines, analytics stacks, and orchestration tools. We love seeing hands-on experience with modern technologies like Snowflake and dbt!
Apply Through Our Website:For the best chance of getting noticed, make sure to apply through our website. It helps us keep track of applications and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at LinkedIn
✨Know Your Data Tools
Make sure you’re well-versed in the tools mentioned in the job description, like AWS, Snowflake, and PySpark. Brush up on your knowledge of data pipelines and be ready to discuss how you've used these technologies in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in data engineering and how you overcame them. Use examples that highlight your ability to design scalable solutions and implement best practices in CI/CD and testing.
✨Understand the Business Context
Research the company’s mission and how data plays a role in their growth strategy. Be ready to discuss how your work as a data engineer can directly impact their product and user experience.
✨Collaborate and Communicate
Since the role involves working closely with analytics, product, and engineering teams, practice articulating your thoughts clearly. Prepare to demonstrate your collaborative mindset and how you’ve successfully worked in cross-functional teams before.
