Senior Product Engineer

About Rudder Stack

The next generation of enterprise software won't be defined by features, it will be defined by craft. As customer data platforms evolve from technical infrastructure to mission-critical business tools, the companies that win will be those that make power feel intuitive and complexity feel invisible.

The AI era demands something enterprises haven't had before: a centralized, warehouse-native source of truth about every customer - clean, unified profiles that can power both human decisions and autonomous AI agents. At Rudder Stack, we've built the data infrastructure that processes 300 billion events every month for enterprises like P&G, Crate & Barrel, and  alongside high-growth AI companies like Vercel, Lovable, n8n, and Assembly

AI. We've proven the architecture works. Now we need full-stack engineers who think like product builders - engineers who can trace a user frustration from a confusing UI through the API layer to the data model, and fix it at every level.

• You'll play a critical role in shaping Rudder Stack's product experiences—not just building interfaces, but understanding the entire customer journey from first click to production deployment. You'll need to master our platform deeply: how event ingestion works, how rETL syncs data back to warehouses, how Profiles resolves identities across devices and builds ML-ready customer models, how Transformations reshape data in flight.
• This isn't a traditional frontend or backend role. It's about owning the full product experience—from the React component a user clicks, through the API that processes their request, to the database query that validates their configuration, all the way to understanding whether this feature actually solves their data activation problem—for humans and AI agents alike.

Your work will directly shape:

• Product-Led Experiences
  :
  Build activation flows for complex features (rETL pipelines, Profile audiences, Transformation logic) that technical and non-technical users can understand and trust
• The AI-Ready Data Layer
  :
  Help design experiences around Profiles—the unified, warehouse-native customer models that become training data for ML and context for AI agents
• The Full Stack
  :
  Own features from database schema design through API implementation to React components—optimizing for both developer experience and end-user delight
• Customer Insight Translation
  :
  Spend time understanding what data engineers struggle with at 2am, what product managers need from audience segmentation, what AI teams need to feed their agents—then build solutions that work
• Platform Mastery
  :
  Become an expert in Rudder Stack's architecture—ingestion pipelines, reverse ETL mechanics, identity resolution algorithms, transformation engines—so you can explain these concepts clearly through UI and build the right abstractions
• Cross-Functional Product Work
  :
  Partner deeply with Product and Design to challenge requirements, propose better user flows, and advocate for technical solutions that balance feasibility with user needs

• You're a Product-Minded Engineer
• Proven experience in building production systems across the full stack
• Genuine curiosity about customer problems
  —you want to join customer calls, read support tickets, understand the "why" behind every feature request
• Willingness to challenge product specs and engage in constructive dialog in order to improve the product for the users.
• You can articulate complex technical concepts (schema evolution, event ordering, identity graphs, ML feature stores) in ways that non-technical users understand

• Comfortable owning features end-to-end: database queries, API design, state management, UI components, deployment
• Experience with modern web stack
  :
  React/Type Script, Node.js, REST APIs, SQL databases (Postgres/Timescale
  
  DB)
• You make pragmatic decisions about where to solve problems—sometimes it's a UI change, sometimes it's a data model fix, sometimes it's better API documentation
• Bonus: experience with data platforms (Snowflake, Databricks, warehouses),
  streaming systems (Kafka, event processing), or ML/AI infrastructure (feature…