Senior/Staff Software Engineer, Data Lifecycle
Decagon · New York City
About this role
## Senior/Staff Software Engineer — Data Lifecycle ### About Decagon Decagon is a conversational AI platform enabling brands to deliver concierge customer experiences. Our technology helps enterprises deploy AI agents that power personalized interactions across voice, chat, email, SMS, and more. We’re an in-office company focused on excellence and velocity, guided by values including **Just Get It Done**, **Invent What Customers Want**, **Winner’s Mindset**, and **The Polymath Principle**. ### About the Team: Data Lifecycle The **Data Lifecycle** team builds the systems that govern how sensitive customer data is handled throughout its lifetime—what we store, who can access it, how we redact it, and when we delete it. This team is core to how Decagon scales: our AI agents work with sensitive information across conversations, tool calls, and customer systems. The frameworks we build protect data consistently across the platform while preserving the context customers need. ### About the Role You’ll design and build the platform infrastructure that lets customers control how their data is **collected, retained, redacted, and deleted**. You’ll partner daily with **product management, governance, and security** to understand customer requirements, define data-handling guarantees, review designs, and iterate through launch. Engineers own features end to end—from technical architecture through deployment and post-launch iteration. You’ll also help ensure sensitive data is protected and that the intended actions actually complete. ### What You’ll Do - Build **PII handling and redaction** systems to identify and protect sensitive information across conversations, agent traces, and other customer data - Design **configuration abstractions** for redaction and retention policies with safe defaults and enterprise flexibility - Build reliable **data deletion workflows** across storage systems and derived data, including retries, verification, and failure visibility - Create **self-serve tools and APIs** so customers can configure, test, and understand data lifecycle policies without relying on an engineer - Build **LLM-backed systems** to scan and detect data anomalies and policy violations - Train and/or tune models for detecting data types including **PII and PHI** - Extend redaction/identification to **multimodal agents** (images, attachments, voice notes, documents) - Implement **monitoring and auditability** to show what data was processed, what remains, and where a policy failed—without exposing sensitive information - Handle data lifecycle systems at scale (backfills, partial failures, safe rollouts) touching critical flows for Fortune 100 companies ### Your Background & Skillset - **4+ years** building software products with strong product thinking - Proficient in **Python**, comfortable working across the stack - Experience designing reliable backend systems and **data pipelines** (async processing, retries, failure recovery) - Understanding of operational maturity for critical systems (observability, monitoring) - Strong instincts for complex systems: where data flows, where copies persist, and what happens on workflow failure - High comfort debugging system failures in deep technical stacks - Judgment on when to move fast vs. when correctness, privacy, and irreversible operations require slowing down ### Even Better - Experience with **PII detection, redaction, tokenization**, or secure handling of sensitive data - Experience buil
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