At a Glance
- Tasks: Build and own an AI-native analytics environment for automated insights from data.
- Company: Join a forward-thinking company at the forefront of data and AI innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic role with opportunities to work on AI-driven projects and collaborate with diverse teams.
- Why this job: Make a real impact by transforming data into actionable insights with cutting-edge technology.
- Qualifications: Strong SQL skills, experience with Snowflake, and proficiency in Python or similar languages.
The predicted salary is between 50000 - 65000 £ per year.
Requirements
- Strong SQL and data modelling expertise
- Experience working with Snowflake
- Experience with analytics engineering tools such as dbt
- Proficiency with Python or similar languages for data workflows
- Experience building and maintaining data pipelines
- Experience translating business questions into analytical models and metrics
- Experience working with analysts, product teams, and business stakeholders to support decision-making
- (Desirable) Experience working with large language models (LLMs) or AI-enabled analytics platforms
- (Desirable) Familiarity with prompt design or AI-assisted analytical workflows
- (Desirable) Familiarity with Snowflake Intelligence
What the job involves
The Data & Insight Engineer will build and own an AI-native analytics environment where insights are generated automatically from data rather than manually through dashboards and reports. This role combines data engineering, analytics engineering, and AI-enabled insight generation.
Responsibilities include:
- Building semantic data models
- Developing automated insight pipelines
- Integrating Snowflake Cortex capabilities to support conversational analytics and AI-driven business intelligence
Data Pipeline Engineering
- Build and maintain robust data ingestion and transformation pipelines
- Integrate data from operational systems into the analytics platform
- Maintain data quality frameworks and validation checks
- Optimise performance of data processing and analytics workloads
AI Insight Pipeline Development and ownership
- Automate recurring analysis traditionally performed manually
- Enable natural-language analytics across curated datasets
- Develop systems that translate business questions into structured data queries
Semantic Data Modelling
- Design and maintain curated business data models that support reliable analytics and AI-driven insights
- Define core business entities, metrics, and KPI definitions
- Build and maintain semantic layers within Snowflake
Governance and Quality Assurance
- Monitor model performance, accuracy, and cost usage
- Implement safeguards to ensure reliable and explainable outputs
- Maintain governance standards for AI-enabled analytics workflows
Data and Insight Engineer in London employer: Orgvue
As a Data and Insight Engineer at our innovative company, you will thrive in a dynamic work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including professional development opportunities and a commitment to employee well-being, all within a cutting-edge AI-native analytics environment located in a vibrant tech hub. Join us to be part of a forward-thinking team that values your contributions and supports your growth in the rapidly evolving field of data analytics.
StudySmarter Expert Advice🤫
We think this is how you could land Data and Insight Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data models, pipelines, and any projects you've worked on. We want to see your expertise in action, so make sure to highlight your experience with SQL, Snowflake, and Python.
✨Tip Number 3
Prepare for those interviews! Brush up on common questions related to data engineering and analytics. We recommend practising how to explain your thought process when translating business questions into analytical models – it’s a key part of the role!
✨Tip Number 4
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 genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Data and Insight Engineer in London
Some tips for your application 🫡
Show Off Your SQL Skills:Make sure to highlight your strong SQL and data modelling expertise in your application. We want to see how you've used these skills in real-world scenarios, so don’t hold back on the details!
Talk About Your Experience with Snowflake:If you've worked with Snowflake, let us know! Share specific projects or tasks where you’ve integrated Snowflake into your workflow. This will help us understand how you can contribute to our AI-native analytics environment.
Demonstrate Your Analytical Mindset:We love candidates who can translate business questions into analytical models. In your application, give examples of how you've done this before, especially if you've worked with analysts or product teams to support decision-making.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining the StudySmarter team!
How to prepare for a job interview at Orgvue
✨Know Your SQL Inside Out
Make sure you brush up on your SQL skills before the interview. Be ready to discuss your experience with data modelling and how you've used SQL in past projects. Practising some common SQL queries can help you feel more confident.
✨Familiarise Yourself with Snowflake
Since this role involves working with Snowflake, it’s crucial to understand its features and capabilities. If you’ve worked with Snowflake before, prepare to share specific examples of how you’ve leveraged it for data pipelines or analytics.
✨Showcase Your Python Skills
If you have experience with Python or similar languages, be prepared to discuss how you've used them in data workflows. Think of a couple of projects where you built or maintained data pipelines and be ready to explain your approach.
✨Understand AI and Analytics Integration
Given the focus on AI-enabled insights, it’s beneficial to have a grasp of how AI can enhance analytics. Familiarise yourself with concepts like large language models and prompt design, and be ready to discuss how they can be applied in a business context.
