At a Glance
- Tasks: Design and maintain scalable data pipelines for AI-powered recruiting.
- Company: Join Context Collective, a leader in AI-driven recruitment solutions.
- Benefits: Flexible remote work, high ownership, and impactful projects from day one.
- Other info: Collaborative culture with opportunities for personal and professional growth.
- Why this job: Shape the future of recruiting with cutting-edge AI and data technologies.
- Qualifications: Experience in data engineering, Python, SQL, and cloud platforms.
The predicted salary is between 60000 - 80000 £ per year.
Context Collective.work is building the next-generation AI-powered sourcing platform for recruiters. Our mission is to help talent teams identify, engage, and hire the best candidates faster through intelligent automation and data-driven insights. We operate at the intersection of data, AI, and recruiting workflows—where high-quality data infrastructure is critical to our success.
Missions
- Design and maintain scalable data pipelines (batch and real-time)
- Build and optimize ETL/ELT workflows across Azure and/or GCP
- Develop data models and architectures to support analytics and ML use cases
- Ensure data quality, integrity, and reliability across systems
- Collaborate with ML engineers to prepare and serve training datasets
- Monitor and improve pipeline performance, cost efficiency, and scalability
- Implement best practices for data governance, security, and compliance
- Contribute to tooling and infrastructure decisions
Tools & Environment
- Cloud: Azure (Data Factory, Synapse) and/or GCP (BigQuery, Dataflow)
- Data Processing: Python, SQL, Spark
- Orchestration: Airflow / Prefect
- Storage: Data lakes, warehouses
- Streaming: Kafka / PubSub (nice to have)
- DevOps: Docker, CI/CD
Working Conditions
- Flexible remote work environment
- Opportunity to work on a product at the cutting edge of AI and recruiting
- High ownership and impact from day one
- Collaborative, product-driven engineering culture
- Opportunity to shape the data foundation of a growing platform
Data Engineer - Permanent employer: Collective.work
At Collective.work, we pride ourselves on being an exceptional employer that fosters a dynamic and inclusive work culture. Our team members enjoy the flexibility of remote work while benefiting from competitive compensation and performance-based incentives. With opportunities for personal and professional growth, employees are encouraged to collaborate directly with founders, making a meaningful impact in expanding our presence across European markets.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Permanent
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with recruiters on LinkedIn. The more people you know, the better your chances of landing that Data Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines, ETL workflows, and any cool projects you've worked on. This is your chance to demonstrate your expertise in Python, SQL, and cloud platforms like Azure or GCP.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your experience with data governance, security, and compliance. Practice common interview questions and think about how you can contribute to the team from day one.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in our mission. Tailor your application to highlight your experience with data models and collaboration with ML engineers, and let us know why you're excited about joining our team.
We think you need these skills to ace Data Engineer - Permanent
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Data Engineer role. Highlight your experience with data pipelines, ETL workflows, and any relevant cloud technologies like Azure or GCP.
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 past projects that demonstrate your expertise in building scalable data solutions.
Showcase Your Technical Skills:Don’t forget to mention your proficiency in tools like Python, SQL, and Spark. If you've worked with orchestration tools like Airflow or have experience in DevOps practices, make sure to include that too!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at Collective.work
✨Know Your Data Tools
Familiarise yourself with the specific tools mentioned in the job description, like Azure Data Factory and GCP BigQuery. Be ready to discuss your experience with these platforms and how you've used them to build scalable data pipelines.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled challenges in data engineering, especially around ETL/ELT workflows. Think about times when you improved pipeline performance or ensured data quality, and be ready to share those stories.
✨Understand the Role of Collaboration
Since this role involves working closely with ML engineers, highlight your teamwork experiences. Discuss how you've collaborated on projects, particularly in preparing datasets for machine learning, and how that contributed to project success.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's data governance practices and how they ensure data integrity. This shows your genuine interest in their processes and helps you assess if their culture aligns with your values.
