# Senior/Staff Software Engineer, Data Lifecycle at Decagon > Onsite · United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Adecagon%3A37a12b9f-fe2d-4379-90e5-ee9f3a52d0cb (read 2026-11-07). ## Facts (observed) - Company: [Decagon](https://alderhq.app/companies/decagon) - Location text: New York City - Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York City") - Posted: 2026-10-28 (9 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/decagon/37a12b9f-fe2d-4379-90e5-ee9f3a52d0cb - Skills listed (inferred): LLMs ## Pay and sponsorship - Posted pay: $200k–$400k per year (from the listing's pay field) - Market band for similar posted roles (New York, n 273): p25 $200k, median $234k, p75 $270k ## Description (the employer's text) ### About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. ### About the Team Data Lifecycle at Decagon 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. We turn complex privacy requirements into reliable infrastructure and product that customers can configure and trust. 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 need to protect that data consistently across the platform, while preserving the context customers need to operate and improve their agents. A failure mode we debug in production becomes a guardrail, a test, or a piece of tooling that catches it everywhere. Our systems run for companies like Hertz, American Airlines, and Notion across chat, voice, email, and SMS. ### About the Role You’ll design and build the platform infrastructure that let customers control how their data is collected, retained, redacted, and deleted. That means strong engineering underneath, and clear abstractions on top that hold up across different enterprise requirements without forking per customer. You’ll work day to day with product management, governance, and security: understanding customer requirements, defining data handling guarantees, reviewing designs, and iterating through launch. You’ll stay close enough to customers to understand their privacy and operational needs in depth and to ensure what you’re building is actually right for them. Engineers here own features end to end, from technical architecture through deployment and the iteration after it. On this team, that includes verifying that sensitive data is protected and the action actually completes. An understanding of durable and fault-tolerant systems is key. ### In this role, you will Build PII handling and redaction systems that identify and protect sensitive information across conversations, agent traces, and other customer data Design configuration abstractions for redaction and retention policies that balance enterprise flexibility with clear, safe defaults Build reliable data deletion workflows that propagate across storage systems and derived data, with retries, verification, and visibility into failures Build self-serve tools and APIs that let customers configure, test, and understand their data lifecycle policies without relying on an engineer Build LLM-backed systems that scan and detect data anomalies and violations Train and/or tune models for detecting data of various forms (including PII and PHI) Extend redaction and identification to multimodal agents, including images, attachments, voice notes, and documents. Build monitoring and auditability that make it clear what data was processed, what remains, and where a policy has failed—without exposing sensitive information in the process Handle data lifecycle systems at scale, including backfills, partial failures, and safe rollouts; your work will touch critical flows for Fortune 100 companies ### Your background and skillset look something like this --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Adecagon%3A37a12b9f-fe2d-4379-90e5-ee9f3a52d0cb and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.