Senior Staff Software Engineer - Detection & Discovery

OneTrust•Atlanta, GA

8d•$165,000 - $247,500•Hybrid

About The Position

OneTrust is hiring a Senior Staff Software Engineer for its Detection & Discovery team. The role balances technical stewardship of the company’s Data Discovery and Data Catalog capabilities with development of AI Detection & Discovery. The engineer will lead architecture and implementation across mature production services and new discovery capabilities, with a focus on reliability, customer impact, and practical modernization. The role includes evolving and maintaining Data Discovery and Data Catalog services, improving the end-to-end discovery pipeline, and leading an engineering-quality initiative to reduce technical debt and modernize code and architecture without compromising stability. It also involves building Java services, APIs, and reusable connectors to discover AI models, agents, and related resources across cloud and AI platforms; normalizing discovered inventory; and enabling continuous, scheduled, and incremental discovery. The engineer will participate in customer-facing production support, troubleshoot issues across services and infrastructure, and drive incidents through root cause analysis and durable corrective action. They will set architecture, lead multi-quarter initiatives, improve code quality and operational maturity, and collaborate across teams while mentoring engineers. OneTrust encourages an office-first culture, with most roles working in the office three days a week; role-specific requirements or flexibility may vary. The listed annual base pay range for candidates based in California, Colorado, Connecticut, Nevada, New York, Rhode Island, and Washington is $165,000–$247,500. The role may also be eligible for discretionary bonuses, equity, and/or commissions. Benefits vary by country.

Requirements

12+ years of professional software engineering experience, including substantial hands-on development in Java and the Spring ecosystem.
Track record designing and delivering distributed, cloud-native services and APIs used in production at enterprise scale.
Experience owning and evolving production software, including reducing technical debt, resolving critical issues, and improving stability and reliability.
Hands-on experience debugging production services in Linux and Kubernetes environments.
Working knowledge of networking and connectivity troubleshooting, including DNS, TLS, HTTP, and proxy or firewall paths.
Experience building integrations with external platforms, including provider APIs, authentication and authorization, pagination, data mapping, and evolving API behavior.
Strong knowledge of distributed systems and workflow or event-driven architectures, including failure handling, retries, idempotency, and observability.
Experience designing secure, multi-tenant SaaS systems, including tenant isolation and secure service-to-service communication.
Demonstrated ability to lead technical strategy across teams, guide architecture decisions, and deliver complex initiatives while remaining effective in code and design reviews.
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Experience with data discovery, data catalog, scanning, ingestion, or asset-inventory platforms.
Experience applying AI or machine learning to discovery, data, or catalog workflows.
Experience with AI platforms and infrastructure, including discovery of models, agents, runtimes, tools, or related cloud resources.
Hands-on experience with AWS, Azure, or Google Cloud APIs and identity models; familiarity with Amazon Bedrock / AgentCore, Azure AI Foundry, or Vertex AI.
Experience with Java workflow orchestration frameworks such as Temporal, connector frameworks, or developer automation.
Experience with Kafka or other messaging technologies, CI/CD, and production operations for cloud-native services.
Familiarity with AI governance, security, privacy, compliance, or other regulated enterprise domains.

Responsibilities

Own the technical evolution and ongoing maintenance of Data Discovery and Data Catalog services and modules, including production support.
Lead an engineering-quality initiative to reduce technical debt, resolve critical issues, and modernize code and architecture while preserving customer stability.
Improve the stability, reliability, performance, and operability of the discovery and catalog pipeline, from scanning and ingestion through downstream use.
Identify and deliver practical opportunities to apply AI to discovery and catalog workflows.
Design and build Java services, APIs, and reusable connectors to discover AI models, agents, and related resources across cloud and AI platforms.
Translate provider APIs, identity models, and resource structures into secure, consistent discovery contracts and normalized inventory data.
Advance continuous, scheduled, and incremental discovery so changes to discovered assets are reflected accurately and promptly.
Enable secure discovery across customer accounts, projects, subscriptions, and regions, and partner with AI Governance teams on asset and lifecycle integrations.
Participate in customer-facing production-support calls for Data Discovery and AI Governance, including issues involving customer-managed or on-premises components.
Troubleshoot issues across Java services, Linux environments, Kubernetes deployments, cloud integrations, and network connectivity, partnering with Support, Operations, and engineering teams to mitigate customer impact.
Drive incidents from investigation through root cause and durable corrective action, using findings to improve product stability, supportability, and documentation.
Set architecture and lead multi-quarter initiatives from design through production adoption; make pragmatic build-versus-reuse decisions.
Establish high standards for code quality, design reviews, automated testing, and production readiness.
Improve observability and operational maturity, using incident patterns and operational metrics to drive systemic improvements.
Collaborate with Product, UX, Security, Platform, and customer-facing teams; mentor engineers and influence practices beyond immediate projects.