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, potential equity, 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 pride ourselves on fostering a culture of innovation and ownership, where every team member has the opportunity to make a significant 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 thrive while tackling meaningful challenges in the AI space.
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 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. Whether it's GitHub repos or personal projects, having tangible examples of your work can really set you apart.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and scenarios. Practice explaining your thought process and problem-solving approach, as this is often what interviewers are looking for.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented Data Engineers, and applying directly can 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, Python, and SQL, and don’t forget to mention any relevant tools like Airflow or dbt. 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! Use it to explain why you’re passionate about data engineering and how you can contribute to our mission. Be sure to mention any specific projects or experiences that showcase your expertise.
Showcase Your Projects:If you’ve worked on any cool data projects, make sure to include them in your application. Whether it’s a personal project or something from your previous job, we love seeing real-world applications of your skills. It gives us a glimpse into your problem-solving abilities!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at SearchWorks
✨Know Your Data Inside Out
Make sure you’re well-versed in the data engineering concepts relevant to the role. Brush up on your Python and SQL skills, and be ready to discuss your experience with data pipelines and transformation frameworks like dbt or Spark. This will show that you can hit the ground running.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles and how you tackled them. Think about times when you improved data quality or optimised performance. This will demonstrate your hands-on experience and ability to innovate, which is key for this position.
✨Familiarise Yourself with Their Tech Stack
Research the tools and technologies mentioned in the job description, such as Airflow, Snowflake, or Databricks. If you have experience with these, be ready to share examples of how you’ve used them effectively. This shows your enthusiasm and readiness to integrate into their existing systems.
✨Prepare Questions That Matter
Think of insightful questions to ask during the interview. Inquire about their data architecture evolution or how they handle customer onboarding. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your values.
