Level

Prolific

Analytics Engineer

Prolific is hiring an Analytics Engineer for a remote role open to applicants in United Kingdom. Level rates it ; you can apply on Level.

AI in this role

claudecursorclaude-code
ai-agents
Analytics Engineer

                    

  Prolific

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.

 

 The role

As an Analytics Engineer in the Data Infrastructure Team at Prolific, you’ll be at the forefront of transforming and maintaining Prolific’s data infrastructure as the way people work with data changes rapidly. Your work will be instrumental in redesigning and scaling our data stack, ensuring data pipelines are robust, efficient, and reliable, and that our data is ready to be queried by people and AI systems alike.   By owning and optimising our analytics codebase and semantic layer, you’ll empower teams across the business to get trustworthy answers, whether through BI dashboards or natural language questions. You’ll help shape how we bring LLMs and agents into analytics engineering safely and effectively, and you’ll bridge the gap between technical and non-technical stakeholders so that complex data solutions are clearly understood and aligned with business goals.     What you’ll bring to the role
  • Expertise in dbt & SQL: Deep experience with dbt and SQL to design, build, and maintain scalable data models.
  • Semantic Modelling: Experience defining metrics, entities, and business logic in a semantic layer (e.g. dbt Semantic Layer/MetricFlow, Cube, LookML or similar), with an understanding of what makes a model reliable for natural language querying.
  • Applied AI in Data Workflows: Hands-on experience using LLMs in data work, such as classification, extraction, or enrichment within transformations, and an understanding of how to evaluate, test, and govern outputs that aren’t fully deterministic.
  • Agentic Tooling: Familiarity with AI coding assistants and agents (e.g. Claude Code, Cursor, MCP-based tooling) and a practical view on where they speed up analytics engineering and where human review is essential.
  • Cloud Technology Knowledge: Strong familiarity with cloud platforms such as AWS or GCP.
  • Data Accuracy Focus: Passion for ensuring high data quality through tests, assertions, evaluations, and robust documentation.
  • Commercial Acumen: Ability to understand business needs and communicate effectively with non-technical stakeholders.
  • Collaboration: Skilled at working cross functionally and translating complex technical concepts into actionable insights for the business.
  • Process-Driven: Proficiency in designing repeatable, scalable workflows for data transformation, including ones that combine automated agents with human oversight.
    What you’ll be doing in the role
  • Building Data Models: Create complex dbt models, custom macros, and reusable packages. Optimize transformations and implement robust testing strategies to ensure data integrity and model performance.
  • Building the Semantic Layer: Design and maintain a semantic layer with well-defined metrics, dimensions, and descriptions so that stakeholders and AI assistants can query company data in natural language and get consistent, accurate answers.
  • Introducing Non-Deterministic Transformations: Build and productionise LLM-powered steps within our pipelines, such as categorizing free text or enriching records, with clear evaluation frameworks, cost and latency monitoring, and fallbacks to keep results trustworthy.
  • Developing Agentic Workflows: Design and adopt agentic workflows across the analytics engineering lifecycle, including model generation, documentation, test coverage, code review, and incident triage, while defining the guardrails and review processes that keep quality high.
  • Ownership: Monitor and maintain dbt workflow jobs, ensuring smooth data refreshes and up-to-date pipelines. You’ll also be responsible for data models powering BI analytics and company-level reporting.
  • Ensuring Data Accuracy: Write tests and assertions to validate data integrity and consistency across models, and build evaluation sets to measure the accuracy of natural language and LLM-driven outputs.
  • Documenting and Standardising: Create and maintain thorough documentation of dbt processes and semantic definitions, treating metadata as a first-class asset since it directly shapes the quality of AI-generated answers.
  • Translating Complex Data Concepts: Act as a key communicator, translating technical data issues into understandable business terms for stakeholders.
  • Mentoring Team Members: Support junior analysts and data engineers, especially in setting up experimentation platforms, adopting AI tooling responsibly, and following data best practices.
  • Collaborating Across Teams: Work closely with product, engineering, and BI teams to ensure data infrastructure supports evolving business needs, including AI-driven products and internal tools.

 

Why Prolific is a great place to work

We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioral data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.

We believe that the next leap in AI capabilities won't come solely from scaling existing models but from integrating diverse human perspectives and behaviors into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation—one that reflects the breadth and the best of humanity.

Working for us will place you at the forefront of AI innovation, providing access to our unique human data platform and opportunities for groundbreaking research. Join us to enjoy a competitive salary, benefits, and remote working within our impactful, mission-driven culture.

 

Links to more information on Prolific

Benefits 

External Handbook 

Website 

Youtube 

 

Privacy Statement

By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal information.

How we rate this

Analytics Engineer at Prolific rates 75 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsClaudeCursorClaude Code

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Walk me through how you've used Claude in your day-to-day work.
  3. What are the limits of Cursor that you've run into, and how did you work around them?
  4. What's a project where you used Claude Code hands-on?
  5. Describe a typical day in a role like this one: which parts run through AI directly?

Adapt your resume

  • List these exact terms on your resume: AI agents, Claude, Cursor, and Claude Code. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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