Analytics Engineer

Prolific; United States; Remote; USD 130,000 - 170,000; Full time

Job summary

Prolific is seeking an Analytics Engineer to build complex dbt models and a semantic layer for natural language querying. You will productionise LLM-powered steps, design agentic workflows, and ensure data integrity across BI analytics.

You will mentor junior engineers, collaborate with product, engineering, and BI teams, and uphold high data quality with robust tests and documentation.

Qualifications

• Deep experience with dbt and SQL to design, build, and maintain scalable data models.
• Experience defining metrics and business logic in a semantic layer (dbt Semantic Layer/MetricFlow, Cube, LookML or similar).
• Hands-on use of LLMs in data work, with evaluation and governance of outputs that aren’t fully deterministic.
• Familiarity with AI coding assistants and agents and where they speed analytics engineering and where human review is essential.
• Strong familiarity with cloud platforms such as AWS or GCP.
• Passion for data quality through tests, assertions, evaluations, and robust documentation.
• Ability to understand business needs and communicate with non-technical stakeholders.
• Skilled at cross-functional collaboration and translating complex technical concepts into actionable insights.
• Design repeatable, scalable data transformation workflows combining automated agents with human oversight.

Responsibilities

• Build complex dbt models, custom macros, and reusable packages with robust tests.
• Design and maintain a semantic layer with defined metrics and dimensions.
• Productionise LLM-powered steps in pipelines with evaluation, cost, latency monitoring, and fallbacks.
• Create agentic workflows across analytics lifecycle, with guardrails and review processes.
• Monitor and maintain dbt workflow jobs and data models powering BI analytics.
• Write tests and assertions to validate data integrity and measure NL/LLM output accuracy.
• Document dbt processes and semantic definitions, treating metadata as a first-class asset.
• Translate technical data issues into clear business terms for stakeholders.
• Mentor junior analysts and data engineers, guiding AI tooling adoption and best practices.

Skills

dbt & SQL

Semantic modelling

Applied AI in data workflows

Agentic tooling

Cloud platforms (AWS/GCP)

Data quality & testing

Business communication

Cross-functional collaboration

Process-driven workflows

Tools

dbt

LookML

Cube

SQL tooling

Job description

Analytics Engineer

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.

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.

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