Remote job
Senior Backend Engineer, Experimentation
Job details
About this role
Role overview
This role focuses on building the backend systems that power a strategic experimentation product, helping engineering organizations make product decisions with real statistical confidence rather than intuition. The work sits at the intersection of large-scale distributed systems, data science, and developer-facing product, spanning experiment setup, event ingestion, metric computation, and results delivery.
Responsibilities
- Design, build, and operate scalable backend services and APIs supporting experimentation workflows - Own backend components end-to-end, including discovery, design, implementation, testing, deployment, monitoring, and incident response - Evolve systems handling event ingestion, metric computation, results processing, and results presentation - Contribute to architectural decisions, weighing tradeoffs around scalability, reliability, performance, cost, and maintainability - Partner with product managers, designers, data scientists, and adjacent engineering teams to translate customer needs into reliable solutions - Investigate production issues, identify root causes, and implement durable fixes - Participate in an on-call rotation within a you-build-it-you-run-it culture - Mentor teammates through code reviews, design reviews, and knowledge sharing
Requirements
- 6+ years of professional software engineering experience, with significant time spent operating production backend services - Fluency in Go or a comparable backend language such as Python, Rust, or C++ - Hands-on experience with distributed systems, event-driven architectures, data pipelines, APIs, and data modeling - Familiarity with analytical or warehouse data stores such as ClickHouse, Postgres, Snowflake, Redshift, BigQuery, or Databricks - Experience diagnosing production issues and improving reliability through metrics, logs, traces, and alerting - Comfort working in cloud environments with infrastructure-as-code tooling - Practical fluency with AI tools in engineering workflows, including the judgment to validate AI-generated output - Clear communication and a collaborative approach with technical and non-technical partners