At a Glance
- Tasks: Build and maintain scalable data pipelines for analytics and product applications.
- Company: Prolific, a leader in AI development and human data infrastructure.
- Benefits: Competitive salary, remote work, and a mission-driven culture.
- Other info: Collaborative environment with opportunities for continuous learning and growth.
- Why this job: Join us to shape the future of AI with impactful data solutions.
- Qualifications: 3+ years in data engineering, strong SQL and Python skills required.
The predicted salary is between 70000 - 90000 £ per year.
Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models.
We're looking for a Senior Data Engineer to help build scalable data solutions that serve teams across the business – from data scientists and analysts to AI engineers and product teams. You'll contribute to both the underlying platform and the way data is used in our products, working in a modern, cloud native environment where privacy, security, and good data practices are part of how we build.
What you’ll bring to the role:
- Technical Expertise: 3+ years building and shipping production grade data systems, with strong SQL skills and proficiency in Python or another object‑oriented language (Java, Scala, etc.). Familiarity with cloud‑native infrastructure – ideally some exposure to Terraform, Kubernetes, or similar IaC and container orchestration tools.
- Experience designing data APIs or services that expose data to applications, or a strong interest in working across the analytical/operational boundary.
- A thoughtful approach to data quality, privacy, and security – you understand that how data is used is as important as how it's moved.
- Strong collaboration skills and comfort working across teams with different priorities – engineers, data scientists, AI engineers, product.
- A pragmatic, curious mindset – you enjoy keeping up with the field and know when to reach for a new tool versus when to stick with what works.
- Pipeline Management: Hands on experience with data pipeline tools (Airflow, dbt) and strong ability to optimise for performance and reliability.
- Quality Focus: Commitment to continuously improving product quality, security, and performance through rigorous testing and code reviews.
- Documentation: Meticulous approach to creating and maintaining architecture and systems documentation.
- Collaborative Mindset: Ability to work across teams to understand and address diverse data needs while maintaining data integrity.
- Growth Orientation: Desire to continually keep up with advancements in data engineering practices and technologies.
- Problem‑Solving: Exceptional analytical skills to troubleshoot complex data issues and implement effective solutions.
- Independence: Capability to ship medium features independently while contributing to the team's overall objectives.
What you’ll be doing in the role:
- Build and maintain data pipelines that power analytics, ML workloads, and product‑facing applications – from internal databases, SaaS sources, and streaming systems through to the teams and services that consume them.
- Evolve our data platform alongside cloud platform engineers, using infrastructure‑as‑code (Terraform) and Kubernetes‑based deployments (Argo) to keep the platform scalable, reliable, and self‑serve.
- System Architecture: Design and implement scalable data infrastructure that accommodates our growing data volume and complexity.
- Develop data services and APIs that expose trusted data to product applications, bridging analytical and operational systems.
- Own data quality and observability, putting monitoring, testing, and alerting in place so issues are caught early and trust in the data stays high.
- Partner with AI engineers, data scientists, analysts, and product teams to understand their data needs and design the right solutions – not just the quickest ones.
- Uphold strong data privacy, security, and compliance practices in everything you build, particularly where data flows into product‑facing contexts.
- Technical Documentation: Create and maintain comprehensive documentation of data flows, models, and systems for knowledge sharing.
- Security and Compliance: Ensure all data systems adhere to security best practices and compliance requirements.
We're offering a competitive salary, benefits, and remote working within a mission‑driven culture.
Senior Data Engineer employer: PARTECH PARTNERS
Prolific is an exceptional employer that fosters a mission-driven culture, offering competitive salaries and benefits while promoting remote working flexibility. As a Senior Data Engineer, you'll thrive in a collaborative environment where your contributions directly impact the development of cutting-edge AI technologies, with ample opportunities for professional growth and continuous learning in a modern, cloud-native setting.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like PARTECH PARTNERS!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Engineer at PARTECH PARTNERS.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like PARTECH PARTNERS.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Engineer at PARTECH PARTNERS, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at PARTECH PARTNERS, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at PARTECH PARTNERS. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at PARTECH PARTNERS
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at PARTECH PARTNERS!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
