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- Tasks: Build and maintain data pipelines for an AI-led intelligence platform.
- Company: Mission-driven tech company focused on sustainability and transparency.
- Benefits: Competitive salary, equity, hybrid working, and growth opportunities.
- Why this job: Join a small team tackling real-world data challenges with impactful solutions.
- Qualifications: Experience in Python, SQL, and building production data pipelines.
- Other info: Collaborative environment with exposure to senior leadership and innovative projects.
The predicted salary is between 60000 - 70000 £ per year.
An early-stage, mission-driven technology company is building an AI-led intelligence platform designed to bring transparency to a highly complex global industry. With sustainability at its core, the platform enables large organisations to make better, more responsible decisions by combining real-time data, pricing intelligence, and policy insight. With an MVP live, early enterprise customers engaged, and strong funding in place, the company is now focused on strengthening the data foundations that underpin the product and its future AI roadmap. This role will be one of the first dedicated data engineering hires.
You will work closely with senior technical leadership to build reliable data pipelines, improve data quality, and enable analytics, product features, and machine-learning use cases as the platform evolves.
Role- Build and maintain data pipelines that ingest, transform, and serve data for product and analytics use cases.
- Integrate multiple internal and external data sources into a clean, reliable data layer.
- Work closely with engineering and product teams to ensure data supports real-world decision making.
- Help establish best practices around data modelling, transformation, and quality checks.
- Contribute to the foundations that will support future machine-learning and AI initiatives.
- Strong experience with Python and SQL.
- Solid data engineering fundamentals and experience building production data pipelines.
- Experience working in AWS or another major cloud environment.
- Comfortable operating in an evolving system where some foundations are still being built.
- A collaborative mindset and interest in understanding how data is used across the product.
- Familiarity with managed or open-source data ingestion tools (for example Fivetran, Airbyte, or similar).
- Exposure to data used in analytics or machine-learning contexts.
- Experience working in early-stage or fast-moving product teams.
- A role with genuine scope to grow as the data function expands.
- Close collaboration with experienced engineering and AI leadership.
- Exposure to technically interesting data problems with real-world impact.
- Equity alongside a competitive salary.
- Hybrid working, typically two days per week in London.
- A small, senior team that values ownership, honesty, and thoughtful engineering.
Unfortunately, sponsorship is not available.
Seniority level: Entry level
Employment type: Full-time
Job function: Information Technology
Location: London, England, United Kingdom
Data Engineer employer: SENZO
Contact Detail:
SENZO Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving Python and SQL. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common data engineering scenarios and be ready to discuss how you've tackled challenges in past projects.
✨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!
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, SQL, and data engineering fundamentals. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to tell us why you’re passionate about data engineering and how you can contribute to our mission. Share your enthusiasm for building reliable data pipelines and improving data quality – we love to see that spark!
Showcase Your Collaborative Spirit: Since this role involves working closely with engineering and product teams, let us know about your experiences in collaborative environments. We value teamwork, so any examples of successful partnerships will definitely catch our eye!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at SENZO
✨Know Your Data Engineering Fundamentals
Brush up on your data engineering basics, especially around building production data pipelines. Be ready to discuss your experience with Python and SQL, as well as any projects where you've integrated multiple data sources.
✨Familiarise Yourself with the Company’s Mission
Understand the company's focus on sustainability and how their AI-led platform aims to bring transparency to the industry. This will help you align your answers with their mission and show that you're genuinely interested in their work.
✨Prepare for Technical Questions
Expect technical questions related to AWS or other cloud environments. Be prepared to explain how you've tackled data quality issues or built reliable data pipelines in your previous roles or projects.
✨Show Your Collaborative Spirit
Since this role involves working closely with engineering and product teams, be ready to share examples of how you've collaborated in the past. Highlight your interest in understanding how data impacts real-world decision-making.