Zapier
Software Engineer — Release Engineering at Zapier
Location: India (Remote)
Employment Type: Full time
Department: Engineering
Compensation
India
- Base Salary – IC3: ₹52,97,600 – ₹79,46,400 • Offers Equity • Offers Bonus
- Base Salary – IC4: ₹64,10,100 – ₹96,15,100 • Offers Equity • Offers Bonus
We believe all Zapiens should be rewarded competitively and equitably, using practices that are simple and transparent. This philosophy ensures we’re able to find, grow, and retain exceptional people from a broad range of backgrounds. Here’s how we define our compensation principles:
- Competitive: Zapier pays well among the technology sector.
- Equitable: Consistent pay practices; Pay for impact
- Simple: Pay is well understood, and pay practices are built for scale.
- Transparent: Zapiens know how pay works, including how their pay is determined.
A Candidate’s compensation package is finalized once the interview process is concluded and accounts for demonstrated experience, job knowledge, skills, abilities, and internal equity. We use a business impact approach to base pay, which means we set pay for all Zapiens based on their demonstrated impact on Zapier’s success. In alignment with that philosophy, the upper half of a pay range is typically reserved for individuals who have consistently demonstrated a high impact in their current role and level while at Zapier.
Application Questions
- Do you have hands-on experience building or maintaining CI/CD pipelines (e.g., GitLab CI, GitHub Actions, Jenkins, ArgoCD, or similar)?
- Do you have 5 or more years of professional software engineering experience that includes meaningful work on CI/CD systems, testing infrastructure, or release tooling? If yes, briefly describe the systems you worked on and your level of ownership.
- Describe a CI/CD pipeline, release system, or internal developer tool you designed or significantly improved. What problem did it solve, what tradeoffs did you evaluate, and what was the measurable impact on the engineers who used it?
- Describe a backend service or API you built from scratch. What were the key technical decisions, and what tradeoffs did you weigh on reliability, performance, or maintainability?
- Tell us about a time you identified a systemic problem in how your team or org was building, testing, or shipping software. What did you do, and what was the measurable outcome?
- Describe a time you had to troubleshoot a failing end-to-end test pipeline. What was the root cause, what tools/techniques did you use to diagnose it, and how did you resolve it?
- Pick one AI workflow you’ve built. Walk us through what triggers it, what it does, and what you had to iterate on.
- Share a specific example where AI changed quality or stakeholder experience — not just speed — and explain what you did to get there.
- What’s one way you’ve expanded your impact at work with AI — what problem were you trying to solve, why did you approach it that way, and how has your approach evolved over time?
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