Full Stack Engineer, Growth

at Stripe
SGD 122,400-183,600 per year
MIDDLE
✅ On-site

Tech Stack

API @ 3 CSS @ 3 Experimentation @ 3 JavaScript @ 3 Machine Learning @ 3 Marketing @ 3 Observability @ 3 Presto @ 2 React @ 3 React, JavaScript Ruby @ 3 Trino @ 2

Details

Stripe is a financial infrastructure platform for businesses. The Growth Engineering team builds systems, products, internal tools, and customer-facing experiences that support product-led growth, including ML-powered recommendations, experimentation engines, multi-channel notification systems, onboarding flows, dashboards, and product recommendations.

Responsibilities

  • Build and maintain scalable systems and customer experiences that help businesses discover, onboard, and grow with Stripe products.
  • Develop robust backend APIs in Ruby that power responsive frontend experiences using React, JavaScript, and CSS.
  • Implement and iterate on ML-driven recommendations, experimentation frameworks, and growth optimization features.
  • Work with data scientists and product managers to translate growth hypotheses into technical implementations and A/B tests.
  • Contribute to shared platforms and tools that enable rapid experimentation across growth initiatives.
  • Debug production issues across the full stack and participate in on-call rotations.
  • Write maintainable, well-tested code that follows team conventions and best practices.

Requirements

Minimum Requirements

  • 2+ years of industry software engineering experience with strong coding skills in any programming language. The interview process is language agnostic.
  • Experience delivering small projects independently and contributing to medium-sized projects with guidance.
  • Strong collaboration skills, including the ability to work across work streams and contribute to peers' success.
  • Ability to thrive with a high level of autonomy and responsibility, along with an entrepreneurial mindset.
  • Deep care for users' needs and the ability to steward great user experiences.

Preferred Qualifications

  • Experience with machine learning, recommender systems, product-led growth, or lifecycle marketing.
  • Familiarity with basic analysis of large datasets, particularly using Redshift, Presto, or Trino.
  • Experience incorporating observability into production systems and supporting their operation through on-call participation and incident response.

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