Data Engineer

Overview

Prolific is hiring a Data Engineer to help build scalable data solutions that support data scientists, analysts, AI engineers, and product teams. The role sits in a modern cloud-native environment where privacy, security, and strong data practices are central to the work.

What You’ll Bring

• 3+ years of experience building and shipping production-grade data systems.
• Strong SQL skills and proficiency in Python or another object-oriented language such as Java or Scala.
• Familiarity with cloud-native infrastructure, ideally with some exposure to Terraform, Kubernetes, or similar infrastructure-as-code and container orchestration tools.
• Experience designing data APIs or services that expose data to applications, or strong interest in working across analytical and operational systems.
• A thoughtful approach to data quality, privacy, and security.
• Strong collaboration skills and comfort working across teams with different priorities.
• A pragmatic, curious mindset and good judgment about when to adopt new tools versus established approaches.
• Hands-on experience with data pipeline tools such as Airflow and dbt, with a focus on performance and reliability.
• Commitment to improving product quality, security, and performance through testing and code reviews.
• Meticulous documentation habits for architecture and systems.
• Strong analytical problem-solving skills for troubleshooting complex data issues.
• Ability to ship medium-sized features independently while contributing to team goals.

What You’ll Do

• Build and maintain data pipelines that support analytics, ML workloads, and product-facing applications, drawing from internal databases, SaaS sources, and streaming systems.
• Work with cloud platform engineers to evolve the data platform using infrastructure as code and Kubernetes-based deployments such as Argo.
• Design and implement scalable data infrastructure that can handle increasing data volume and complexity.
• Develop data services and APIs that expose trusted data to product applications and connect analytical and operational systems.
• Own data quality and observability through monitoring, testing, and alerting so issues are detected early.
• Partner with AI engineers, data scientists, analysts, and product teams to understand their data needs and build appropriate solutions.
• Apply strong privacy, security, and compliance practices, especially where data flows into product-facing contexts.
• Create and maintain clear documentation of data flows, models, and systems.
• Ensure data systems follow security best practices and compliance requirements.