At a Glance
- Tasks: Build and own data pipelines while collaborating with AI experts on impactful projects.
- Company: Join a globally leading AI lab for an exciting short-term project.
- Benefits: Gain hands-on experience, work in a dynamic team, and enhance your skills.
- Other info: 40 hours per week commitment from 14 September to 10 October 2026.
- Why this job: Make a real impact on AI models while developing your data engineering expertise.
- Qualifications: Proficiency in SQL and Python, with experience in ETL, ELT, and data warehousing.
The predicted salary is between 63000 - 77000 £ per year.
Mercor is recruiting UK-Based Data Engineering experts for a short, intensive project with a globally leading AI lab.
What the work involves:
You will produce the kind of professional documents and deliverables you create day to day in your own field, working alongside experts from the other two domains. Those outputs are then logged and used to build the tasks and rubrics that train and evaluate frontier AI models, so your professional judgement is the core of what makes the work valuable.
Who we are looking for:
- Data Engineers: hands-on data engineers who build and own pipelines in production.
- Titles such as Data Engineer, Senior or Staff Data Engineer, Analytics Engineer, Data Platform Engineer and Data Architect.
- We are looking for a core stack of SQL and Python, with experience across ETL and ELT, orchestration, streaming and data warehousing, using tools such as Spark, Kafka, Airflow, dbt, Snowflake, BigQuery or Databricks on AWS, GCP or Azure.
- Some roles focus on hands-on delivery, others on documenting and structuring that work.
Commitment:
- 40 hours per week, running from 14 September to 10 October 2026.
- The project team collaborates on a UK business-hours rhythm.
- You should not be committed to other projects requiring significant hours during this period. Smaller side projects can be discussed.
The vetting process involves:
- Resume and work history screen.
- A short assessment evaluating domain expertise.
Please note: Candidates must be based in the United Kingdom and have the right to work in the United Kingdom. This is an essential requirement for the role.
Data Engineer - Pipeline Specialist in London employer: Obsidian
Obsidian is an exceptional employer located in the vibrant Greater London area, offering a dynamic work culture that fosters innovation and collaboration among experts in the field. Employees benefit from a fast-start program with opportunities for growth and extension, alongside a commitment to quality in AI model training that makes a meaningful impact in genomics. With a focus on professional development and a supportive environment, Obsidian is dedicated to empowering its team members to excel in their careers.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Pipeline Specialist in London
✨Tap into Online Data Science Communities
Join online communities focused on data science like Kaggle, LinkedIn groups, or Reddit threads. These are goldmines for temporary gigs, as you can network with professionals and potentially hear about opportunities at companies like Obsidian before they're even advertised!
✨Show Off Your Skills With Projects
Got some cool data science projects? Showcase them on platforms like GitHub or create a personal portfolio website. This visibility is crucial for landing temporary roles—let recruiters see your actual skills in action, which can set you apart from the crowd.
✨Check Out Specialist Job Boards
For temp roles, hit up job boards dedicated to tech and data science, like Stack Overflow Jobs or DataJobs. These platforms often feature openings that you won’t find on general job sites, including contracts with companies like Obsidian.
✨Leverage University Resources
If you're currently at uni or recently graduated, tap into your school's career services. They often have connections with companies looking for temporary data science interns or contract workers, and they might even host job fairs with employers like Obsidian.
We think you need these skills to ace Data Engineer - Pipeline Specialist in London
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at Obsidian, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!
Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Obsidian, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
Craft a Unique Data-Driven Cover Letter:Instead of the usual generic cover letter, spice it up with some data! Maybe you’ve improved a process by 20% in a past role or cleaned a dataset with over a million entries. Use these stats to your advantage to grab Obsidian’s attention and show the tangible impact of your work.
How to prepare for a job interview at Obsidian
✨Showcase Your Analytical Skills
For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at Obsidian.
✨Brush Up on Technical Skills
You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
✨Highlight Your Adaptability
Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Obsidian.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Obsidian.
