Lead Analytics Engineer

Lead Analytics Engineer

Data

                    

  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 a Analytics Engineer in the Data Infrastructure Team at Prolific, you'll be at the forefront of transforming and maintaining Prolific's data infrastructure. Your work will be instrumental in redesigning and scaling our data stack, ensuring data pipelines are robust, efficient, and reliable. By owning and optimizing our analytics codebase, you'll empower our BI-focused teams to deliver valuable insights to the entire organization, while driving data accuracy and accessibility. You'll also bridge the gap between technical and non-technical stakeholders, making sure 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.
• Cloud Technology Knowledge: Strong familiarity with cloud platforms like AWS, GCP etc
• Data Accuracy Focus: Passion for ensuring high data quality through tests/assertions and robust documentation.
• Commercial Acumen: Ability to understand business needs and communicate effectively with non-technical stakeholders.
• Mentorship Ability: Advocate for best practices in logging and data modeling that supports robust and effective analysis, reporting , and experimentation.
• Collaboration: Skilled at working cross-functionally and translating complex technical concepts into actionable insights for the business.
• Process-Driven: Proficiency in designing repeatable and scalable workflows for data transformation.

What you’ll be doing in the role 

• Building Data Models: Create complex dbt models, custom macros, and reusable packages. Optimise transformations and implement robust testing strategies to ensure data integrity and model performance.
• Ownership: Monitoring and maintaining dbt workflow jobs, ensuring smooth data refreshes and up-to-date pipelines.You will also be responsible for data models for BI analytics & company level reporting 
• Ensuring Data Accuracy: Writing tests and assertions to validate data integrity and consistency across models. 
• Documenting and Standardizing: Creating and maintaining thorough documentation of dbt processes to ensure best practices within the BI team.
• Translating Complex Data Concepts: Acting as a key communicator, translating technical data issues into understandable business terms for stakeholders.
• Mentoring Team Members: Supporting junior analysts and data engineers, especially in setting up experimentation platforms and data best practices.
• Collaborating Across Teams: Working closely with the product, engineering, and BI teams to ensure data infrastructure supports evolving business needs.

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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