[Hiring] Lead Analytics Engineer @Prolific

Role Description

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.

Qualifications

• 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.

Requirements

• 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. 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.

Benefits

• Competitive salary
• Benefits
• Remote working opportunities
• Impactful, mission-driven culture