Senior Growth Engineer

Apollo.io is the leading go-to-market solution for revenue teams, trusted by over 500,000 companies and millions of users globally, from rapidly growing startups to some of the world's largest enterprises. Founded in 2015, the company is one of the fastest growing companies in SaaS, raising approximately $250 million to date and valued at $1.6 billion. Apollo.io provides sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies worldwide, along with tools to engage and convert these contacts in one unified platform. By helping revenue professionals find the most accurate contact information and automating the outreach process, Apollo.io turns prospects into customers. Apollo raised a series D in 2023 and is backed by top-tier investors, including Sequoia Capital, Bain Capital Ventures, and more, and counts the former President and COO of Hubspot, JD Sherman, among its board members.

Your Role & Mission

We’re looking for a Senior Growth Engineer to join our team and accelerate our product’s growth through rapid experimentation, data analysis, and scalable technical solutions. You’ll work at the intersection of engineering, product, and marketing to optimize funnels, automate growth loops, and implement features that drive measurable impact.

Daily Adventures & Responsibilities

• Own and drive growth initiatives end-to-end — identify opportunities, design experiments, ship solutions, and measure impact on key metrics (NRR, GRR, CAC, activation, and expansion).
• Design and implement experimentation frameworks (A/B testing, feature flags, instrumentation) to ensure reliable measurement and fast iteration.
• Collaborate cross-functionally with Product, Marketing, Data, and Engineering to translate growth hypotheses into shipped, measurable features.
• Leverage data and AI to personalize user experiences, optimize engagement, and reduce friction across the user journey.
• Raise the technical bar by ensuring growth experiments are production-quality, performant, secure, and maintainable.
• Act as a technical leader in growth — influencing roadmap decisions, mentoring others, and helping define best practices for experimentation and analytics.
• Develop and execute engineering strategies that enable rapid experimentation and data-driven decision-making.
• Architect and build scalable systems that support growth initiatives, such as onboarding flows, personalization engines, and viral loops.
• Ensure technical excellence through best practices in performance optimization, security, and scalability.

Skills & Relevant Experience

• 7+ years of professional software engineering experience, with significant ownership of shipped product features.
• Strong full-stack experience, including:
  • Frontend: JavaScript/Typescript, React
  • Backend: RoR, Node.js, Python, or similar
  • Databases, APIs, and cloud infrastructure (AWS/GCP)
• Hands-on experience with analytics and experimentation:
  • A/B testing frameworks (e.g LaunchDarkly, Optimizely, Amplitude, Google Analytics, or custom)
  • Analytics platforms (Amplitude, Mixpanel, statsig, etc)
• Proven ability to use data to drive decisions, not just report on it
• Excellent leadership, communication, and stakeholder management skills.
• AI & Modern Tooling
  • Practical experience using AI/LLMs to:
    • Accelerate development
    • Build product features
    • Improve productivity
• Mindset:
  • Outcome-oriented: You care about business impact, not just shipping code
  • Experimental: You’re comfortable forming hypothesis, testing quickly, and iterating based on real data
  • Biased toward action: You thrive in ambiguity and move forward with imperfect information
  • Metrics-driven: Obsession with metrics (LTV, CAC, retention) and rapid iteration
  • High ownership: You treat growth problems as yours to solve
• Nice-to-Haves:
  • Experience on a growth, product-led, or experimentation team
  • Background at a startup or scaling product
  • Prior experience mentoring or leading engineers in ambiguous problem spaces
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