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 salary, equity options, and rapid growth opportunities.
- Other info: Fully remote role with a collaborative and innovative culture.
- 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.
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
- 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
- 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 pride ourselves on fostering a culture of innovation and ownership, where every team member has the opportunity to make a meaningful impact. With fully remote working options, competitive compensation, and a focus on employee growth, we provide a collaborative environment that empowers our Data Engineers to shape the future of our AI-powered products. Join us to work with cutting-edge technology and be part of a talented team dedicated to transforming a complex industry.
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 virtual meetups, and connect with fellow data enthusiasts 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, including any cool pipelines or dashboards you've built. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your Python and SQL skills, and be ready to discuss your experience with tools like Airflow and dbt. Practising common data engineering interview questions can help you feel more confident when it’s time to shine.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented Data Engineers who are ready to make an impact. Plus, applying directly can sometimes give you a better chance of getting noticed by our hiring team.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your experience with data pipelines, transformation frameworks, and any relevant tools like Python and SQL. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for data engineering and how you can contribute to our mission of transforming industries with AI. Let us know why you're excited about this opportunity at StudySmarter.
Showcase Your Projects:If you've worked on any cool data projects, don’t hold back! Include links or descriptions of your work that demonstrate your ability to design and build data solutions. We love seeing real-world applications 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 your application and ensures you get all the updates directly from us. Plus, it shows you're keen on joining 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 Python, SQL, and any orchestration tools you've used, like Airflow or Prefect. This will show that you understand the core responsibilities of the role.
✨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 experience with data quality checks and monitoring, as well as any innovative solutions you've implemented. This demonstrates your hands-on approach and ability to think critically.
✨Familiarise Yourself with Their Tech Stack
Research the technologies mentioned in the job description, such as Snowflake, BigQuery, or Databricks. If you have experience with these tools, be ready to discuss it. If not, show your willingness to learn and adapt to new technologies, especially in a fast-paced environment.
✨Emphasise Collaboration and Communication
Since the role involves working closely with product, engineering, and AI teams, be prepared to talk about your collaborative experiences. Share how you've effectively communicated complex data concepts to non-technical stakeholders, which is crucial for ensuring everyone is aligned on project goals.
