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For Companies At a Glance
- Tasks: Design and maintain scalable data pipelines while generating actionable insights.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Negotiable salary, flexible working, professional development funding, and standard benefits.
- Why this job: Be at the forefront of AI and data engineering, making a real impact.
- Qualifications: Degree in STEM or equivalent experience; strong programming skills required.
- Other info: Diverse and inclusive workplace with excellent growth opportunities.
The predicted salary is between 36000 - 60000 ÂŁ per year.
As a Analytics & AI Data Engineer within the Data Team, you will sit at the intersection of data engineering and analytics—designing robust, scalable data foundations while generating insights that support operational and strategic decision‑making. The role provides end‑to‑end ownership of data workflows, including ingestion, transformation, modelling, analysis, and testing. You will also play a key role in advancing our AI‑enabled data capabilities, including unstructured data handling, vector search, LLM‑ready architectures, and AI‑assisted engineering practices.
Core Purpose
To build and maintain scalable data pipelines and platforms, and to analyse and interpret data to generate insights, reports, and recommendations that deliver business value.
The day to day:
- Design, build, and maintain ETL/ELT pipelines for ingesting, transforming, and storing data from multiple sources.
- Ensure data quality, integrity, and reliability through automated testing and validation.
- Manage and optimise databases, data warehouses, and cloud data environments (e.g. Azure/AWS).
- Collaborate with Data Operations to ensure platform stability and operational excellence.
Analytics & Insight
- Collect, clean, and analyse structured and unstructured data to identify trends and actionable insights.
- Develop dashboards and reports using BI tools such as Power BI or Tableau.
- Communicate findings clearly to both technical and non‑technical audiences.
- Prepare datasets for AI/ML use cases, including feature engineering, dataset shaping, and data labelling.
AI & Advanced Data Capabilities
- Design and enhance pipelines supporting unstructured data, vector embeddings, and semantic search.
- Contribute to data architectures that enable LLM integrations, AI agents, and cloud‑native AI workloads.
- Apply AI‑assisted engineering practices, such as code generation, documentation automation, and quality checks.
- Use AI‑enabled analytical tooling to accelerate pattern discovery, validation, and problem investigation.
- Work closely with Data Operations and Data Services Leads to balance priorities and resource allocation.
- Partner with Technical Leads to ensure solutions align with established technical guardrails and best practices.
- Engage with business stakeholders to understand requirements and translate them into deliverable solutions.
- Collaborate with Data Scientists and AI Engineers on model deployment, vector database integration, and monitoring.
- Champion a culture of learning, innovation, and process optimisation.
- Proactively introduce new tools, automation opportunities, and analytical approaches.
- Explore emerging frameworks and implement practical improvements.
Governance & Compliance:
- Ensure all data activities comply with governance, privacy, and security standards.
- Contribute to data management initiatives, documentation, and best practices.
About you
- Degree in a STEM subject or equivalent experience.
- Strong programming skills (Python, SQL, R, or similar).
- Experience with cloud data platforms (Azure, AWS, GCP) and big data technologies (Spark, Hadoop).
- Knowledge or experience with Denodo is an advantage.
- Proficiency in BI and data visualisation tools (Power BI, Tableau).
- Solid understanding of data modelling, ETL/ELT processes, and database management.
- Analytical mindset with strong problem‑solving and communication skills.
- Ability to work collaboratively across multidisciplinary teams and engage with stakeholders at all levels.
- Commitment to continuous learning and professional development.
- Awareness of modern AI/LLM concepts and the ability to support AI‑ready data engineering, including vector embeddings, semantic search, and use of AI service APIs (Azure OpenAI, Gemini, etc.).
- Experience shaping data for advanced analytics or ML, including feature extraction and dataset quality checks.
- Understanding of cloud‑based AI workloads and MLOps deployment and monitoring patterns.
The rewards
- A negotiable basic salary and all the normal benefits you’d expect (Holiday, company pension etc.)
- A collaborative, open and honest environment that is designed to deliver the best outcomes to our clients and staff.
- A flexible working methodology to enable you to be where you need to be, if you don’t need to be in an office then don’t, if you want to be in an office your welcome to use one.
- An environment built around supporting and developing our staff with funding available for relevant professional qualifications.
- We are an Equal Opportunity Employer. We take pride in the diversity of our team and seek diversity in our applicants.
Analytics & AI Data Engineer employer: Brown & Brown UK
Contact Detail:
Brown & Brown UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics & AI Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those involving AI and analytics. It’s a great way to demonstrate what you can do beyond the application.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions related to data engineering and AI. Practise explaining complex concepts in simple terms—this will impress both techies and non-techies alike!
✨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!
We think you need these skills to ace Analytics & AI Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Analytics & AI Data Engineer role. Highlight your programming skills, cloud experience, and any relevant projects that showcase your data engineering and analytics capabilities.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about data engineering and analytics. Share specific examples of how you've tackled challenges in previous roles and how you can contribute to our team at StudySmarter.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in tools like Python, SQL, and BI platforms such as Power BI or Tableau. We want to see how you’ve used these skills in real-world scenarios, so be specific!
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at Brown & Brown UK
✨Know Your Data Inside Out
Before the interview, dive deep into the data engineering concepts relevant to the role. Brush up on ETL/ELT processes, data modelling, and cloud platforms like Azure or AWS. Being able to discuss your experience with these technologies will show that you're not just familiar but truly knowledgeable.
✨Showcase Your Analytical Skills
Prepare to discuss specific examples where you've collected, cleaned, and analysed data to generate insights. Bring along any dashboards or reports you've created using BI tools like Power BI or Tableau. This will demonstrate your ability to communicate findings effectively to both technical and non-technical audiences.
✨Emphasise Collaboration
This role requires working closely with various teams, so be ready to share experiences where you've collaborated with data scientists, AI engineers, or business stakeholders. Highlight how you’ve balanced priorities and contributed to team success, as this will resonate well with their emphasis on teamwork.
✨Stay Ahead of AI Trends
Familiarise yourself with modern AI concepts and how they relate to data engineering. Be prepared to discuss your understanding of vector embeddings, semantic search, and AI service APIs. Showing that you’re proactive about learning and implementing new tools will set you apart as a candidate who’s ready for the future.