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 options, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement opportunities in a collaborative environment.
- 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, and integrating Snowflake Cortex capabilities to support conversational analytics and AI-driven business intelligence.
Responsibilities include:
- 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 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 environment located in a vibrant tech hub. Join us to be part of a forward-thinking team that is shaping the future of AI-driven analytics and making a meaningful impact on business decision-making.
StudySmarter Expert Advice🤫
We think this is how you could land Data and Insight Engineer
✨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 help you land that Data and Insight Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your SQL expertise, data models, and any cool projects you've done with Snowflake or Python. We want to see how you translate business questions into actionable insights, so make it shine!
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of analytics engineering tools like dbt and be ready to discuss your experience with data pipelines. We recommend practising common interview questions and even doing mock interviews with friends.
✨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 about their job search. Let’s get you that Data and Insight Engineer position!
We think you need these skills to ace Data and Insight Engineer
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 keep track of your application and ensure it gets the attention it deserves. We can’t wait to see what you bring to the table!
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 prepared 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, be ready to share specific examples of how you’ve utilised it in your previous roles.
✨Showcase Your Analytics Engineering Skills
Be prepared to talk about your experience with analytics engineering tools like dbt. Think of a project where you successfully built or maintained data pipelines and be ready to explain your approach and the impact it had on decision-making.
✨Demonstrate Your AI Knowledge
If you have experience with large language models or AI-enabled analytics platforms, make sure to highlight that. Discuss any relevant projects or insights you've gained from working with AI technologies, as this could set you apart from other candidates.
