Data Engineer

Data 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

We're looking for a Data Engineer to help build scalable data solutions that serve teams across the business—from data scientists and analysts to AI engineers and product teams. You'll contribute to both the underlying platform and the way data is used in our products, working in a modern, cloud-native environment where privacy, security, and good data practices are part of how we build.

What you’ll bring to the role

Technical Expertise: 3+ years building and shipping production-grade data systems, with strong SQL skills and proficiency in Python or another object-oriented language (Java, Scala etc.)

Familiarity with cloud-native infrastructure—ideally some exposure to Terraform, Kubernetes, or similar IaC and container orchestration tools. We don't expect deep expertise in all of these, but comfort working in this kind of environment matters.

Experience designing data APIs or services that expose data to applications, or a strong interest in working across the analytical/operational boundary.

A thoughtful approach to data quality, privacy, and security—you understand that how data is used is as important as how it's moved.

Strong collaboration skills and comfort working across teams with different priorities—engineers, data scientists, AI engineers, and product.

A pragmatic, curious mindset—you enjoy keeping up with the field and know when to reach for a new tool versus when to stick with what works.

Pipeline Management: Hands-on experience with data pipeline tools (Airflow, dbt) and strong ability to optimize for performance and reliability.

Quality Focus: Commitment to continuously improving product quality, security, and performance through rigorous testing and code reviews.

Documentation: Meticulous approach to creating and maintaining architecture and systems documentation.

Problem-Solving: Exceptional analytical skills to troubleshoot complex data issues and implement effective solutions.

Independence: Capability to ship medium features independently while contributing to the team's overall objectives.

What you’ll be doing in the role

Build and maintain data pipelines that power analytics, ML workloads, and product-facing applications—from internal databases, SaaS sources, and streaming systems through to the teams and services that consume them.

Evolve our data platform alongside cloud platform engineers, using infrastructure-as-code (Terraform) and Kubernetes-based deployments (Argo) to keep the platform scalable, reliable, and self-serve.

System Architecture: Design and implement scalable data infrastructure that accommodates our growing data volume and complexity.

Develop data services and APIs that expose trusted data to product applications, bridging analytical and operational systems.

Own data quality and observability, putting monitoring, testing, and alerting in place so issues are caught early and trust in the data stays high.

Partner with AI engineers, data scientists, analysts, and product teams to understand their data needs and design the right solutions—not just the quickest ones.