Analytics Engineer

Overview

Prolific is hiring an Analytics Engineer for its Data Infrastructure Team. The role focuses on strengthening and scaling the company’s data infrastructure so data is reliable, queryable, and useful for both people and AI systems. You’ll work on dbt models, semantic layers, LLM-assisted data workflows, and agentic tooling, while helping teams across the business get trustworthy answers from data.

This is a remote role with UK-based working requirements.

What You’ll Do

• Build and maintain complex dbt models, custom macros, and reusable packages.
• Optimize data transformations and improve model performance and reliability.
• Design and maintain a semantic layer with clear metrics, dimensions, descriptions, and business logic.
• Support natural-language querying by making models dependable for BI tools and AI assistants.
• Introduce LLM-powered steps into data pipelines, such as categorization, extraction, and enrichment.
• Create evaluation frameworks, monitor cost and latency, and implement fallbacks for non-deterministic outputs.
• Develop agentic workflows across model generation, documentation, test coverage, code review, and incident triage.
• Define guardrails and review processes to keep AI-assisted work high quality.
• Monitor and maintain dbt workflow jobs and data refreshes.
• Own data models that support BI analytics and company-level reporting.
• Write tests, assertions, and evaluation sets to validate data integrity and LLM-driven outputs.
• Document dbt processes and semantic definitions so metadata remains accurate and useful.
• Translate technical data issues into clear business language for stakeholders.
• Support junior analysts and data engineers in adopting AI tooling and data best practices.
• Collaborate with product, engineering, and BI teams on evolving data needs and AI-driven products.

Requirements

• Deep experience with dbt and SQL for scalable data modeling.
• Experience defining metrics, entities, and business logic in a semantic layer such as dbt Semantic Layer/MetricFlow, Cube, LookML, or similar.
• Hands-on experience using LLMs in data workflows for tasks like classification, extraction, or enrichment.
• Ability to evaluate, test, and govern outputs that are not fully deterministic.
• Familiarity with AI coding assistants and agents such as Claude Code, Cursor, or MCP-based tooling.
• Strong familiarity with cloud platforms such as AWS or GCP.
• Focus on data quality through tests, assertions, evaluations, and documentation.
• Ability to understand business needs and communicate effectively with non-technical stakeholders.
• Experience working cross-functionally and translating technical concepts into actionable insights.
• Ability to design repeatable, scalable workflows that combine automation with human oversight.