Analytics Engineer

Plutus
2 weeks ago
Create job alert

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. Optimize 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.

Core Skills:SQL, ETL, dbt

Other Skills:

Seniority:Mid

#J-18808-Ljbffr

Related Jobs

View all jobs

Analytics Engineer

Analytics Engineer

Analytics Engineer

Analytics Engineer

Analytics Engineer 3 (Revenue Operations), null

Senior Analytics Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Navigating Data Science Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Data science has taken centre stage in the modern workplace. Organisations rely on data-driven insights to shape everything from product innovation and customer experience to operational efficiency and strategic planning. As a result, there is a growing need for skilled data scientists who can analyse large volumes of data, build predictive models, communicate findings effectively, and collaborate cross-functionally. If you are looking to accelerate your data science career—or even land your first role—attending data science career fairs can be a game-changer. Unlike traditional online applications, face-to-face interactions let you showcase your personality, passion, and communication skills in addition to your technical expertise. However, to stand out in a busy environment, you need a clear strategy: from polishing your personal pitch and asking thoughtful questions to following up with a memorable message. In this article, we’ll guide you through every step of making a strong impression at data science career fairs in the UK and beyond.

Common Pitfalls Data Science Job Seekers Face and How to Avoid Them

Data science has become a linchpin for decision-making and innovation across countless industries, from finance and healthcare to tech and retail. The demand for data scientists in the UK continues to climb, with businesses seeking professionals who can interpret complex datasets, build predictive models, and communicate actionable insights. Despite this high demand, the job market can be extremely competitive—and many applicants unknowingly fall into avoidable traps. Whether you’re an aspiring data scientist fresh out of university, a professional transitioning from a quantitative role, or a seasoned analyst looking to expand your skill set, it’s crucial to navigate your job search effectively. In this article, we explore the most common pitfalls data science job seekers face and provide pragmatic advice to help you stand out. By refining your CV, portfolio, interview strategies, and communication skills, you can significantly increase your chances of landing a rewarding data science role. If you’re looking for your next data science job in the UK, don’t forget to explore the listings at Data Science Jobs. Read on to discover how to avoid critical mistakes and position yourself for success.

Career Paths in Data Science: From Entry-Level Analysis to Leadership and Beyond

Data is the lifeblood of modern business, and Data Scientists are the experts who turn raw information into strategic insights. From building recommendation engines to predicting market trends, the impact of data science extends across virtually every industry—finance, healthcare, retail, manufacturing, and beyond. In the UK, data-driven decision-making is critical to remaining competitive in a global market, making data science one of the most sought-after career paths. But how does one launch a career in data science, and how can professionals progress from entry-level analysts to senior leadership roles? In this comprehensive guide, we’ll explore the typical career trajectory, from junior data scientist to chief data officer, discussing the key skills, qualifications, and strategic moves you need to succeed. Whether you’re a recent graduate, transitioning from another technical field, or an experienced data scientist aiming for management, you’ll find actionable insights on forging a successful career in the UK data science sector.