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- Tasks: Join a team to build clean, modular data pipelines and transformations.
- Company: Be part of a rapidly growing AI FinTech revolutionising data-driven solutions.
- Benefits: Enjoy a hybrid work model and a modern tech stack with no legacy issues.
- Why this job: Shape a greenfield platform and work with cutting-edge technologies in a low-fighting environment.
- Qualifications: 2+ years of Python experience, strong DBT skills, and SQL proficiency required.
- Other info: Ideal for tech enthusiasts with a passion for innovation and personal projects.
The predicted salary is between 36000 - 60000 £ per year.
We’re hiring on behalf of a rapidly growing AI FinTech who are building the next-generation data-driven solutions. This is a brilliant opportunity for a hands-on Python Data Engineer with 2+ years' experience to join a modern, low-firefighting environment focused on clever, high-quality development - not legacy clean-up.
As a core contributor to a greenfield data platform, you’ll work closely with data scientists and engineers to deliver clean, modular, production-grade data pipelines and transformations. Your work will influence how real-time financial data is captured, transformed, and utilised.
Experience:
- Python (2+ years experience): Strong scripting and implementation skills
- Confident with Pandas
- DBT: Regular use of DBT in current role (not just setup experience)
- Creating reusable DBT modules
- SQL: Comfortable writing, optimizing, and deploying SQL through DBT models
Nice-to-Haves:
- Snowflake experience (ideal but not essential – platform skills can be learned)
- Exposure to Airflow for orchestration
Would Love to See:
- A Computer Science degree or similar technical background
- Active GitHub profile showcasing personal or collaborative projects
- Passion for tech and data beyond the 9-5 (side projects, contributions, or personal innovations)
Why Join?
- Greenfield Development: No tangled legacy mess - help shape a clean, smart system from the ground up
- Modern Stack: Snowflake, DBT, Python, Airflow - no tech debt weighing you down
- Low-Firefighting Environment: Spend your time building, not fixing
- Quality Over Volume: Small but impactful data - live, intelligent, and real-time
Python Data Engineer - AI FinTech (DBT, SQL) - London / Hybrid employer: GL Global
Contact Detail:
GL Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Python Data Engineer - AI FinTech (DBT, SQL) - London / Hybrid
✨Tip Number 1
Familiarise yourself with the latest trends in AI FinTech and how Python is being used to drive innovation in this space. This knowledge will not only help you during interviews but also demonstrate your genuine interest in the industry.
✨Tip Number 2
Engage with the DBT community by participating in forums or contributing to open-source projects. This will enhance your skills and show potential employers that you're proactive and passionate about your craft.
✨Tip Number 3
Build a portfolio of projects that showcase your ability to create clean, modular data pipelines using Python and DBT. Highlighting these projects can set you apart from other candidates and provide concrete examples of your capabilities.
✨Tip Number 4
Network with professionals in the AI FinTech sector through LinkedIn or local meetups. Making connections can lead to valuable insights about the company culture and potentially even referrals for the job.
We think you need these skills to ace Python Data Engineer - AI FinTech (DBT, SQL) - London / Hybrid
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, DBT, and SQL. Include specific projects or roles where you used these technologies, especially in a data engineering context.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI FinTech and how your skills align with the company's goals. Mention any relevant side projects or contributions that showcase your enthusiasm for tech and data.
Showcase Your GitHub Profile: If you have an active GitHub profile, include it in your application. Highlight any personal or collaborative projects that demonstrate your coding skills and familiarity with data engineering tools.
Highlight Relevant Experience: When detailing your work experience, focus on your hands-on experience with DBT and SQL. Provide examples of how you've created reusable DBT modules or optimised SQL queries in previous roles.
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 implemented Python solutions, especially focusing on your scripting and implementation skills. If possible, bring examples of your work or code snippets to demonstrate your expertise.
✨Demonstrate DBT Proficiency
Since the role requires regular use of DBT, be ready to explain how you've used it in your current or previous roles. Discuss any reusable DBT modules you've created and how they improved your data pipeline processes. This will show your hands-on experience and understanding of best practices.
✨SQL Knowledge is Key
Prepare to talk about your SQL experience, particularly in writing, optimising, and deploying SQL through DBT models. You might be asked to solve a problem or optimise a query during the interview, so brush up on your SQL skills and be ready to think on your feet.
✨Passion for Tech and Data
Let your enthusiasm for technology and data shine through. Share any side projects, contributions to open-source, or personal innovations that showcase your passion beyond your day job. This can set you apart from other candidates and demonstrate your commitment to continuous learning.
