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Prolific

Platform matching researchers with study participants

Analytics Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote in UKNo salary listed

About the Job

Requirements

  • Deep experience with dbt and SQL to design, build, and maintain scalable data models.
  • Experience defining metrics, entities, and business logic in a semantic layer, such as dbt Semantic Layer/MetricFlow, Cube, LookML, or similar, with an understanding of what makes a model reliable for natural language querying.
  • Hands-on experience using large language models in data work, such as classification, extraction, or enrichment within transformations, and an understanding of how to evaluate, test, and govern outputs that are not fully deterministic.
  • Familiarity with AI coding assistants and agents, such as Claude Code, Cursor, and MCP-based tooling, and a practical view on where they speed up analytics engineering and where human review is essential.
  • Strong familiarity with cloud platforms such as AWS or GCP.
  • Focus on ensuring high data quality through tests, assertions, evaluations, and robust documentation.
  • Ability to understand business needs and communicate effectively with non-technical stakeholders.
  • Ability to work cross-functionally and translate complex technical concepts into actionable insights for the business.
  • Proficiency in designing repeatable, scalable workflows for data transformation, including workflows that combine automated agents with human oversight.

Responsibilities

  • Create complex dbt models, custom macros, and reusable packages; optimize transformations and implement robust testing strategies to ensure data integrity and model performance.
  • Design and maintain a semantic layer with well-defined metrics, dimensions, and descriptions so stakeholders and AI assistants can query company data in natural language and receive consistent, accurate answers.
  • Build and productionize large-language-model-powered steps within pipelines, such as categorizing free text or enriching records, with evaluation frameworks, cost and latency monitoring, and fallbacks to keep results trustworthy.
  • Design and adopt agentic workflows across the analytics engineering lifecycle, including model generation, documentation, test coverage, code review, and incident triage, while defining guardrails and review processes that maintain quality.
  • Monitor and maintain dbt workflow jobs to ensure smooth data refreshes and up-to-date pipelines; maintain data models powering BI analytics and company-level reporting.
  • 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.
  • Create and maintain thorough documentation of dbt processes and semantic definitions, treating metadata as a first-class asset because it shapes the quality of AI-generated answers.
  • Translate technical data issues into understandable business terms for stakeholders.
  • Support junior analysts and data engineers, especially in setting up experimentation platforms, adopting AI tooling responsibly, and following data best practices.
  • Work closely with product, engineering, and BI teams to ensure data infrastructure supports evolving business needs, including AI-driven products and internal tools.

About the Company

Prolific

Platform matching researchers with study participants

Prolific operates an online research platform that connects researchers with a global pool of vetted participants. Researchers post studies and set demographic criteria to recruit eligible participants, who complete surveys and other research tasks to earn compensation. The platform uses a reputation-based system to ensure data quality from both researchers and participants. Clients include academic institutions, businesses, and individual researchers, making Prolific a centralized marketplace for human data in online research. The business model charges researchers a commission for each study conducted on the platform, covering participant recruitment, study management, and platform upkeep. Overall, Prolific aims to provide a reliable, efficient way to collect high-quality data for research and market insights.

Company size
1,001-5,000
Company stage
Series A
Total funding
$33.3M
Headquarters
London, United Kingdom
Founded
2014

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