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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 Watford employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land (197990) AWS Senior Data Engineer in Watford
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with AWS or similar tech. 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 pipelines and projects. Use platforms like GitHub to share your code and demonstrate your expertise in Python, SQL, and AWS services. This will make you stand out when applying through our website.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering scenarios. Be ready to discuss how you've tackled real-time analytics challenges and implemented CI/CD practices. We want to see your problem-solving skills in action!
✨Tip Number 4
Don’t forget to follow up after interviews! A quick thank-you email can keep you fresh in the interviewer's mind. Plus, it shows your enthusiasm for the role and the company. Let’s make sure they remember you!
We think you need these skills to ace (197990) AWS Senior Data Engineer in Watford
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Senior Data Engineer. Highlight your experience with AWS, Python, and SQL, and don’t forget to mention any relevant projects that showcase your skills in building data pipelines.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data engineering and how your background aligns with our mission at StudySmarter. Keep it concise but impactful!
Showcase Your Technical Skills: When detailing your experience, be specific about the tools and technologies you've used. Mention your familiarity with services like S3, Glue, and orchestration tools like Airflow. We want to see your technical prowess!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Harnham
✨Know Your Data Tools
Make sure you’re well-versed in the tools mentioned in the job description, like AWS services, PySpark, and Snowflake. 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 share specific examples of how you've tackled complex data challenges. Think about high volume, low latency scenarios you've worked on and how you ensured reliability and scalability in your solutions.
✨Understand the Company’s Data Vision
Research the company’s growth strategy and how data plays a role in it. Be ready to discuss how you can contribute to their data journey and what best practices you would implement to enhance their analytics platform.
✨Collaborate and Communicate
Since the role involves working closely with various teams, practice articulating your thoughts clearly. Prepare to discuss how you’ve collaborated with product and engineering teams in the past to drive better decision-making through data.