Analytics Engineer

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.