At a Glance
- Tasks: Design and build data pipelines, transform data, and create analytics solutions.
- Company: Fast-growing tech company revolutionising industries with AI and modern data infrastructure.
- Benefits: Flexible working, competitive pay, equity options, and modern tools.
- Other info: Fully remote role with opportunities for rapid growth and collaboration.
- Why this job: Join a dynamic team and make a real impact in the AI space.
- Qualifications: 5+ years in data engineering, strong Python and SQL skills required.
The predicted salary is between 80000 - 125000 £ per year.
Our client is a fast-growing, venture-backed technology company transforming a large, complex industry through artificial intelligence and modern data infrastructure. We’re building AI-powered products that combine advanced automation with human expertise to improve operational workflows, reduce friction, and create better customer experiences at scale. As an early-stage team, we’re focused on solving meaningful challenges using modern technology and creating a culture where ownership, innovation, and impact matter.
We’re looking for a Data Engineer to build and scale the data foundations that power our platform, customer experience, analytics, and AI systems.
Fully Remote - £80,000 - £125,000
The Role:
As a Data Engineer, you'll design and operate the infrastructure that moves, transforms, and structures data across our systems. You'll work across ingestion pipelines, transformation layers, monitoring, customer onboarding, and analytics while partnering closely with product, engineering, and AI teams. This is a hands-on role with meaningful ownership and the opportunity to influence how data architecture evolves as the business scales.
Responsibilities:
- Design and build reliable end-to-end data pipelines connecting customer systems, internal platforms, and AI-generated outputs
- Create and maintain transformation layers that convert raw data into trusted, documented datasets
- Develop dashboards and analytics solutions for both internal teams and customer-facing insights
- Implement monitoring, alerting, and data quality checks to proactively identify issues
- Lead customer data onboarding by mapping datasets and building scalable integration processes
- Improve performance and optimize warehouse costs as data volumes grow
- Collaborate with backend, AI, and product teams to align operational and analytical systems
- Establish best practices for data modeling, testing, documentation, schema management, and code review
- Support and improve AI-driven workflows and data capabilities
Requirements:
- 5+ years of experience in data engineering within production environments
- Strong Python and SQL skills
- Experience with workflow orchestration tools such as Airflow, Dagster, Prefect, or similar
- Hands-on experience with transformation frameworks such as dbt or Spark
- Experience with cloud data warehouses such as Snowflake, BigQuery, Redshift, or Databricks
- Strong understanding of data modeling principles across analytical and operational systems
- Experience with monitoring and data quality tooling
- Familiarity with CI/CD practices and testing for data workflows
- Comfortable working with emerging technologies, including AI-assisted development tools
What We Offer:
- Flexible working arrangements
- Competitive compensation and potential equity participation
- Modern tools and equipment
- Opportunities for rapid growth and meaningful impact
- Collaborative and highly talented team environment
Data Engineer employer: SearchWorks
As a fast-growing technology company, we offer an exceptional work environment that prioritises innovation, ownership, and meaningful impact. Our fully remote setup allows for flexible working arrangements, while our competitive compensation package and potential equity participation ensure that your contributions are recognised and rewarded. Join a collaborative team of talented professionals dedicated to transforming industries through cutting-edge AI and data solutions, with ample opportunities for rapid growth and personal development.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just chat with folks on LinkedIn. Building relationships can open doors to opportunities that aren’t even advertised.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects or contributions to open-source. This gives potential employers a taste of your skills and how you tackle real-world problems.
✨Ace the Interview
Prepare for those interviews by practising common questions and scenarios related to data engineering. Think about how you can demonstrate your problem-solving skills and technical knowledge in a way that aligns with their needs.
✨Apply Through Our Website
When you find a role that excites you, 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 reflects the skills and experiences that match the Data Engineer role. Highlight your Python, SQL, and data engineering experience to show us you’re the right fit!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data engineering and how you can contribute to our mission. Share specific examples of your past work that align with our needs.
Showcase Your Projects:If you've worked on any relevant projects, don’t hesitate to include them! We love seeing real-world applications of your skills, especially those involving data pipelines and analytics.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at SearchWorks
✨Know Your Data Engineering Fundamentals
Brush up on your data engineering principles, especially around data pipelines and transformation frameworks. Be ready to discuss your experience with tools like Airflow or dbt, and how you've used them in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of challenges you've faced in previous roles and how you tackled them. Highlight your ability to optimise performance and manage data quality, as these are key aspects of the role.
✨Familiarise Yourself with Their Tech Stack
Research the technologies mentioned in the job description, such as Snowflake or BigQuery. If you have experience with similar tools, be ready to explain how you can leverage that knowledge to benefit their systems.
✨Emphasise Collaboration and Communication
Since the role involves working closely with product, engineering, and AI teams, be prepared to discuss how you've successfully collaborated in the past. Share examples of how you’ve communicated complex data concepts to non-technical stakeholders.
