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For Companies 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 focused on data-driven growth.
- Benefits: Competitive salary, remote work, and clear progression opportunities.
- Other info: Work remotely with a dynamic team tackling complex data challenges.
- Why this job: Make a real impact on a modern data platform and engineering culture.
- Qualifications: Strong experience in data engineering with Python, SQL, and AWS.
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.
(197990) AWS Senior Data Engineer in Nottingham employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land (197990) AWS Senior Data Engineer in Nottingham
✨Tip Number 1
Network like a pro! Reach out to people in the industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those using AWS and modern analytics stacks. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering challenges. Be ready to discuss how you’d tackle real-time analytics and data pipeline issues – they’ll love your problem-solving mindset!
✨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’re always looking for passionate data engineers like you!
We think you need these skills to ace (197990) AWS Senior Data Engineer in Nottingham
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experience mentioned in the job description. Highlight your AWS, Python, and SQL expertise, and don’t forget to showcase any relevant projects that demonstrate your data engineering prowess.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about this role and how your background aligns with the company’s goals. Be genuine and let your personality come through!
Showcase Your Projects: If you've worked on any interesting data projects, make sure to mention them! Whether it's building data pipelines or using modern analytics stacks, sharing specific examples can really set you apart from other candidates.
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 don’t miss out on any important updates during the process!
How to prepare for a job interview at Harnham
✨Know Your Data Engineering Stuff
Make sure you brush up on your data engineering skills, especially in AWS. Be ready to discuss your experience with building data pipelines, using services like S3 and Glue, and how you've tackled real-time analytics challenges.
✨Show Off Your Coding Skills
Since advanced Python and SQL are key for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code and be ready to explain your thought process.
✨Familiarise Yourself with Modern Tools
Get comfortable with the tools mentioned in the job description, like Snowflake, dbt, and orchestration tools like Airflow. Being able to discuss how you've used these tools in past projects will show you're a great fit for their tech stack.
✨Prepare for Collaboration Questions
This role involves working closely with analytics, product, and engineering teams. Think of examples from your past where you've successfully collaborated across teams, and be ready to share how you contributed to better decision-making through data.