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For Companies At a Glance
- Tasks: Design and scale real-time data architectures and machine learning pipelines.
- Company: Next-gen AI startup at the forefront of real-time intelligence systems.
- Benefits: Competitive salary, equity, and a culture of autonomy and creativity.
- Other info: Join a fast-paced environment with massive growth potential and talented teams.
- Why this job: Be the first Data Engineer and shape the future of AI-driven solutions.
- Qualifications: Experience in building scalable data pipelines and a passion for AI and ML.
The predicted salary is between 36000 - 60000 £ per year.
I’m currently partnered with a next-generation AI startup that’s building real-time intelligence systems capable of understanding and adapting to the world as it happens. They’re at the forefront of streaming data, continuous learning, and large-scale inference - and they’re now looking for a Data Engineer to help power that mission. This isn’t a maintenance role. It’s a build-it-from-the-ground-up opportunity in a company that’s solving genuinely hard, technical problems around real-time data pipelines and ML infrastructure.
The Opportunity
The team is looking for a Data Engineer who can design and scale real-time data architectures and machine learning pipelines that drive continuous model training and experimentation. You’ll be working in close collaboration with ML engineers, product leads, and platform teams to make sure the company’s AI models are learning, and improving, in production.
What You’ll Be Doing
- Architect and maintain real-time streaming data systems (Kafka, Kinesis, or Flink)
- Build robust feature pipelines using Airflow, Prefect, or Dagster
- Manage and optimise data storage solutions (Snowflake, BigQuery, Redshift, or Delta Lake)
- Automate and scale model training pipelines in close partnership with ML engineers
- Deploy, observe, and improve pipelines using Docker, Kubernetes, Terraform, or dbt
- Champion data reliability, scalability, and performance across the platform
The Tech Environment
You’ll likely be working with some combination of:
- Languages: Python, Scala, Go
- Streaming: Kafka / Flink / Spark Structured Streaming
- Workflow orchestration: Airflow / Prefect / Dagster
- Data storage & processing: Snowflake / Databricks / BigQuery / Redshift
- Infrastructure: Docker / Kubernetes / Terraform / dbt
- Monitoring: Prometheus / Grafana / OpenTelemetry
- Cloud: AWS / GCP / Azure
What They’re Looking For
- Proven experience building scalable data pipelines in real-time or near real-time environments
- Strong background in data architecture, performance tuning, and distributed systems
- Comfort working end-to-end - from data ingestion to model-ready outputs
- An interest in AI, ML ops, and data-driven product development
- Someone who thrives in fast-moving, high-ownership start-up environments
Why This Role?
- Join a well-funded AI startup with massive growth potential
- Be the first Data Engineer, shaping the entire data backbone
- Work with exceptionally talented engineers and researchers solving real-world AI problems
- Competitive package, plus meaningful equity
- A culture that values autonomy, creativity, and continuous learning
Interested? If you’ve built or scaled data pipelines in production and want to be part of something genuinely cutting-edge, I’d love to chat. Drop me a message here on LinkedIn or send over your details - happy to share more about the team, the vision, and what they’re building.
Locations
AI Data Engineer employer: SR2 | Socially Responsible Recruitment | Certified B Corporation
Contact Detail:
SR2 | Socially Responsible Recruitment | Certified B Corporation Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Data Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the AI and data engineering space on LinkedIn. Join relevant groups, attend meetups, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving real-time data pipelines or ML models. Share it on GitHub or your personal website, and make sure to highlight any relevant technologies you’ve worked with.
✨Tip Number 3
Tailor your approach! When reaching out to potential employers, mention specific projects or technologies from their job description that you’re passionate about. This shows you’ve done your homework and are genuinely interested in what they’re building.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s a great way to get noticed by the hiring team!
We think you need these skills to ace AI Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Engineer. Highlight your experience with real-time data systems and any relevant tech you've worked with, like Kafka or Airflow. We want to see how your skills align with our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for AI and ML, and explain why you’re excited about building data pipelines from the ground up. Let us know how you can contribute to our innovative team.
Showcase Your Projects: If you've worked on any cool projects related to data engineering, don’t hold back! Include links or descriptions of your work that demonstrate your ability to design and scale data architectures. We love seeing practical examples of your skills.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get the attention you deserve. Plus, it’s super easy to do!
How to prepare for a job interview at SR2 | Socially Responsible Recruitment | Certified B Corporation
✨Know Your Tech Stack
Familiarise yourself with the specific technologies mentioned in the job description, like Kafka, Airflow, and Docker. Be ready to discuss your hands-on experience with these tools and how you've used them to build scalable data pipelines.
✨Showcase Your Problem-Solving Skills
Prepare examples of real-world challenges you've faced in data engineering. Highlight how you approached these problems, the solutions you implemented, and the impact they had on the projects. This will demonstrate your ability to tackle the hard technical problems the company is solving.
✨Understand the AI and ML Landscape
Brush up on your knowledge of AI and machine learning concepts, especially how they relate to data engineering. Be prepared to discuss how you can contribute to continuous model training and experimentation, as this is a key part of the role.
✨Emphasise Collaboration
Since you'll be working closely with ML engineers and product leads, highlight your teamwork skills. Share experiences where you've successfully collaborated across teams to achieve a common goal, showcasing your ability to thrive in a fast-moving startup environment.