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- Tasks: Build and optimise data pipelines using Python, Airflow, and DBT.
- Company: Join a fast-growing London-based AI tech scale-up focused on innovative data solutions.
- Benefits: Enjoy a dynamic work environment with opportunities for growth and collaboration.
- Why this job: Be at the forefront of AI innovation and shape the future of data infrastructure.
- Qualifications: Strong Python skills, experience with DBT, Airflow, and Snowflake required.
- Other info: This is a unique chance to join a greenfield project from the ground up.
The predicted salary is between 43200 - 72000 £ per year.
Are you a passionate Data Engineer with deep Python skills and a love for building robust data pipelines? Want to be at the forefront of innovation, helping shape the future of AI-driven platforms? We’re partnering with a high-growth London-based technology scale-up that is investing heavily in its data capabilities. With a greenfield data platform underway, this is your chance to join at the beginning of an exciting journey.
You’ll be instrumental in defining, building, and optimising the company’s new ML/AI data infrastructure. Working alongside engineering and product stakeholders, you'll help shape and scale the data function from the ground up.
- Architect and implement modern, scalable data pipelines using Python, Airflow, and DBT.
- Design high-quality, governed data models to support a fast-moving AI product team.
- Collaborate with data scientists and ML engineers to deliver clean, reliable data.
- Drive best practices across data quality, testing, observability, and CI/CD.
- Influence technical decisions and contribute to the long-term vision for the platform.
Strong hands-on engineering skills in Python (clean, production-grade code is a must). Experience working with DBT and orchestrating data workflows with Airflow. Deep SQL knowledge and experience working with Snowflake (or similar). Passion for collaborating across teams and a strong interest in AI/ML platforms. A degree in Computer Science or a related technical field (or equivalent experience).
Python Engineer (Data Engineering) employer: GL Global
Contact Detail:
GL Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Python Engineer (Data Engineering)
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Python, DBT, and Snowflake. Consider building a small project or contributing to open-source projects that utilise these tools to showcase your hands-on experience.
✨Tip Number 2
Network with professionals in the data engineering field, especially those who work with AI-driven platforms. Attend meetups or webinars related to data engineering and AI to make connections and learn about industry trends that could give you an edge.
✨Tip Number 3
Prepare to discuss your previous experiences in building data pipelines and collaborating with cross-functional teams. Be ready to share specific examples of how you've driven best practices in data quality and CI/CD processes in your past roles.
✨Tip Number 4
Stay updated on the latest advancements in AI and machine learning, as this role involves working closely with ML engineers. Being knowledgeable about current trends and technologies will demonstrate your passion for the field and your commitment to contributing to the company's vision.
We think you need these skills to ace Python Engineer (Data Engineering)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python skills and experience with data engineering tools like DBT, Airflow, and Snowflake. Use specific examples of projects where you've built data pipelines or collaborated with teams on AI/ML initiatives.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data engineering and AI. Mention why you're excited about the opportunity to work with a fast-growing tech scale-up and how your background aligns with their goals.
Showcase Relevant Projects: If you have any personal or professional projects that demonstrate your ability to architect and implement data pipelines, include them in your application. This could be through a portfolio or links to GitHub repositories.
Highlight Collaboration Skills: Since the role involves working closely with data scientists and ML engineers, emphasise your teamwork and communication skills. Provide examples of how you've successfully collaborated on projects in the past.
How to prepare for a job interview at GL Global
✨Showcase Your Python Skills
Be prepared to discuss your experience with Python in detail. Highlight specific projects where you've built data pipelines or worked on data engineering tasks. Demonstrating your ability to write clean, production-grade code will be crucial.
✨Familiarise Yourself with DBT and Airflow
Since the role requires experience with DBT and Airflow, make sure you understand how these tools work. Be ready to explain how you've used them in past projects, and consider discussing any challenges you faced and how you overcame them.
✨Understand Snowflake and SQL
Brush up on your SQL skills and be prepared to discuss your experience with Snowflake or similar platforms. You might be asked to solve a problem or optimise a query during the interview, so practice your SQL queries beforehand.
✨Demonstrate Collaboration Skills
This role involves working closely with data scientists and ML engineers, so be ready to talk about your collaborative experiences. Share examples of how you've successfully worked in cross-functional teams and contributed to shared goals.
