Analytics Engineer

Prolific Academic Ltd; United States; A distancia; USD 110.000 - 150.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote-friendly culture

Descripción de la vacante

Prolific is hiring an Analytics Engineer to lead data infrastructure work. You’ll redesign and scale our data stack, build robust data models, and empower teams to query data for BI dashboards and NL questions.

You’ll own the analytics codebase, develop a semantic layer, and productionise LLM-powered transformations with guardrails. Remote work options are available within the US.

Formación

• Expertise with dbt and SQL to design scalable data models.
• Experience defining metrics and semantic layers for BI and AI.
• Hands-on use of LLMs in data transformations and governance.
• Familiarity with AI tooling and agentic workflows in analytics.
• Cloud platforms (AWS or GCP) knowledge.
• Commitment to data quality with tests and documentation.
• Ability to communicate with non-technical stakeholders.
• Strong collaboration across teams and clear business impact.
• Design repeatable, scalable data transformation workflows.

Responsabilidades

• Build complex dbt models, macros, and reusable packages; optimize transformations and tests.
• Design and maintain a semantic layer with well-defined metrics for NL querying.
• Productionise LLM-powered steps in pipelines with evaluation and fallbacks.
• Develop agentic workflows across analytics lifecycle with guardrails and reviews.
• Monitor dbt workflow jobs and maintain data models powering BI and reporting.
• Write tests and assertions to validate data integrity across models.
• Document dbt processes and semantic definitions; treat metadata as an asset.
• Translate complex data concepts into actionable business terms.
• Mentor junior analysts and data engineers on experimentation and AI tooling.
• Collaborate with product, engineering and BI teams to support evolving needs.

Conocimientos

dbt & SQL

Semantic Modelling

Applied AI in Data Workflows

Agentic Tooling

Cloud Platforms (AWS/GCP)

Data Quality & Documentation

Commercial Acumen

Cross-functional Collaboration

Process-driven Workflows

Herramientas

dbt Semantic Layer / MetricFlow

Cube

LookML

Descripción del empleo

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.

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