At a Glance
- Tasks: Design and optimise data pipelines while supporting AI-driven initiatives.
- Company: Join a forward-thinking organisation investing in cutting-edge data platforms.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic role with a focus on collaboration and career advancement.
- Why this job: Shape the future of data solutions and work with innovative AI technologies.
- Qualifications: Experience in AWS data engineering and a passion for AI/ML concepts.
The predicted salary is between 60000 - 80000 £ per year.
We’re looking for an experienced Business Intelligence Engineer / AWS Data Engineer to support the development of a modern data platform and contribute to emerging AI-driven initiatives. This role will suit someone with a strong foundation in AWS data engineering and BI, alongside practical exposure to AI/ML concepts or tools. This is an organisation investing heavily in its data platform and future AI capabilities, where you’ll play a key role in shaping both current BI delivery and next-generation data solutions.
Role Split
- ~50% Business Intelligence / Data Engineering delivery & support
- ~50% New capability development, including data platform and AI initiatives
RESPONSIBILITIES
- Design, build and optimise data pipelines and data platform components within AWS
- Support and enhance Business Intelligence reporting and analytics capabilities
- Contribute to the development of a data lake and modern data architecture
- Work across both BAU support and new capability development
- Collaborate with wider teams on AI-related initiatives and roadmap delivery
- Apply best practices around data quality, governance and performance optimisation.
Experience and Skills
Essential Skills
- AWS Data Engineering stack (e.g. Glue, S3, Lambda, Redshift, Athena)
- SQL (advanced level)
- Python (for data processing and pipeline development)
- Infrastructure / tooling exposure (e.g. Terraform, APIs, CI/CD beneficial)
- Experience working in Business Intelligence / analytics environments
- AI / ML Exposure (Key Requirement)
Candidates must demonstrate some level of exposure to AI/ML, such as:
- Working with cloud-based AI services (e.g. AWS Bedrock or similar)
- Supporting AI-enabled data products or workflows
- Understanding of generative AI / prompt engineering concepts
- Exposure to ML pipelines or collaborating with Data Science teams
This does not need to be a core specialism but must be clearly evidenced and practical. Typically 4–8+ years’ experience in data engineering / BI roles. Proven experience delivering AWS-based data solutions. Background in data warehousing, analytics, or data platform development. Experience working in complex or regulated environments is beneficial.
Desirable Skills
- Knowledge of AWS AI services (e.g. Bedrock)
- Experience contributing to data lake builds or modern data platforms
- Exposure to DataOps / CI-CD practices
- Public sector experience (nice to have).
Business Intelligence Engineer (AWS / AI Exposure) employer: Evodia Limited
Join a forward-thinking organisation that prioritises innovation and employee development, particularly in the realm of data and AI. As a Business Intelligence Engineer, you'll benefit from a collaborative work culture that encourages creativity and continuous learning, while being part of a team dedicated to building cutting-edge data solutions. With significant investment in technology and a focus on professional growth, this role offers a unique opportunity to shape the future of data analytics in a dynamic environment.
StudySmarter Expert Advice🤫
We think this is how you could land Business Intelligence Engineer (AWS / AI Exposure)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AWS data engineering projects or any AI-related work you've done. This gives potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common BI and AWS questions. Practice explaining your past projects and how they relate to the role. Confidence is key, so get comfortable talking about your experience!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Business Intelligence Engineer (AWS / AI Exposure)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with AWS data engineering and any AI/ML exposure. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or tools you've worked with!
Craft a Compelling Cover Letter:Your cover letter is your chance to tell us why you’re the perfect fit for this role. Share your passion for data and AI, and explain how your background makes you a great candidate. Keep it engaging and personal!
Showcase Your Projects:If you’ve worked on any cool data projects, especially those involving AWS or AI, make sure to mention them! We love seeing practical examples of your work, so include links or descriptions that highlight your contributions.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re genuinely interested in joining our team at StudySmarter!
How to prepare for a job interview at Evodia Limited
✨Know Your AWS Inside Out
Make sure you brush up on your knowledge of the AWS Data Engineering stack, especially tools like Glue, S3, and Redshift. Be ready to discuss how you've used these in past projects, as this will show your practical experience and understanding of the platform.
✨Showcase Your AI/ML Exposure
Since AI exposure is a key requirement, prepare specific examples of how you've worked with AI/ML concepts or tools. Whether it's using AWS Bedrock or collaborating with Data Science teams, having concrete examples will demonstrate your capability and interest in this area.
✨Prepare for Technical Questions
Expect technical questions around SQL and Python, as well as data pipeline development. Practise coding challenges or scenarios that might come up during the interview, so you can confidently showcase your skills when asked.
✨Understand the Company’s Vision
Research the organisation's current data initiatives and future AI capabilities. Being able to discuss how you can contribute to their goals will not only impress them but also show that you're genuinely interested in the role and the company.
