Staff Software Engineer, API and Collectibles Data TeamAbout the RoleWe are looking for two Staff Software Engineers to set the technical direction for the API and Collectibles Data Team, the engineering team responsible for turning Collectors' collectibles data into products: the external APIs that partners and customers build on, and the spec data that identifies every item we grade, authenticate, and sell.
This team owns the external-facing APIs and API gateway layer, developer portals, the spec data models and ingestion pipelines behind our collectibles catalog, and the integrations that downstream products depend on. Its work serves PSA, Beckett, SGC, Card Ladder, marketplace and research teams, and external partners across millions of collectible specs and certificates.
As a Staff Software Engineer, your influence extends beyond your team to the technology organization. You will have expertise in the entirety of the team's portfolio, including how our services interact and how data flows between them, and you will build the same understanding of adjacent systems across grading, marketplace, vault, and identity.
This is a high-impact, high-visibility role. Every product that shows or prices a collectible depends on the data and APIs this team ships. You will own architecture decisions, API strategy, and the quality bar for everything that ships.
We believe there is significant value in onsite partnership and team collaboration, and candidates within 1-hour commuting distance of the office may be expected to work onsite most of the time. Additional details will be discussed during the recruiting process.
What You'll Do- Own the architecture of the API and data domain end to end: Hold expertise across the entirety of the team's products and services, including Java and Python services, the distributed event-driven systems they run on in AWS and Kubernetes, their relational and NoSQL data stores, and the consumers they serve. Architect for iterative, autonomous development and future scaling through clean service boundaries, contract design, clear data ownership, and well-sequenced migration paths.
- Set the API strategy: Define standards for API design, versioning, authentication, rate limiting, and documentation so that internal teams and external partners can build against our APIs with confidence. Near-term work includes piloting a certificate lookup API with a business admin console, launching a self-service developer portal, migrating legacy public API users, and consolidating API gateways under consistent rate limiting and bot protection.
- Build and evolve the collectibles data platform: Design the data models, pipelines, and access patterns that keep collectibles data accurate, consistent, and fast to query across product lines. Near-term work includes enriching spec data across all categories with measurable quality scoring, unifying overlapping spec databases across PSA, Beckett, and SGC, and exposing the result through a spec API.
- Set the bar for reliability and quality: Establish standards for testing strategy, observability, performance, and incident response, then use operational data to drive stability and latency improvements.
- Lead complex, multi-quarter delivery: Lead projects of moderate-to-significant scope spanning 1-2 quarters, coordinating engineers, stakeholders, and dependencies to a predictable landing. Plan using an explicit value-vs-cost framework so urgency and prioritization are easily understood by the team and stakeholders alike, and surface blockers and delays to the right people before they become surprises.
- Drive cross-functional execution: Translate business requirements into technical solutions and partner across Product, Marketplace, Grading, Vault, Data, and platform teams. Build a working model of adjacent systems, including CRM, identity, and data/ETL, so you catch cross-system risks early.
- Multiply the engineers around you: Mentor senior and mid-level engineers through design reviews, pairing, and feedback. Foster a culture of knowledge sharing and documentation within your team and with business stakeholders by initiating and facilitating meaningful discussions around complex issues.
- Lead an AI-first approach to building: Establish standards and guardrails for AI coding tools, including code review expectations, testing requirements, and security and IP hygiene. Identify where AI agents accelerate the team's work, and raise the org's collective AI fluency by sharing proven workflows and coaching engineers on when the tooling helps and when it misleads.
Who You Are- 8+ years engineering experience, including demonstrated technical leadership of multi-engineer, multi-quarter projects. You have a track record of designing, shipping, and scaling products with measurable business impact at a high-growth company or a scaled consumer platform.
- Expert-level backend engineering in Java and Python. You have strong proficiency in Java (Spring Boot) and Python, own complex API surfaces, async patterns, and data consistency in distributed systems, and are recognized by peers and leaders as an authority in your domain.
- API design depth. You have designed and operated REST (and, where relevant, GraphQL or gRPC) APIs at scale, with strong judgment on versioning, backward compatibility, authentication, and developer experience.
- Deep database and data-modeling expertise, both relational (e.g. PostgreSQL) and NoSQL (e.g. DynamoDB). Schema design, migrations, query performance, and the realities of shared-database coupling between services. Experience with data ingestion and enrichment pipelines, and with API gateways such as Kong, is a plus.
- Strong distributed-systems fundamentals. Service boundaries, contract design, Kafka-based event-driven architectures, messaging, idempotency, and failure handling, designed and operated in production.
- Production AWS and Kubernetes depth, with infrastructure-as-code fluency (CDK and/or Terraform).
- Full-stack awareness. Enough hands-on experience with modern frontend frameworks such as React to design and review at every layer, even where others do the bulk of the implementation.
- A track record of raising engineering standards across testing strategy, observability, documentation, and incident response.
- Observability-first mindset. You instrument what you ship using Datadog or similar tools, build dashboards and alerting, and use distributed tracing to debug production issues.
- AI-first engineer. Daily fluency with AI coding assistants and agentic tools (Claude Code, Copilot, Cursor, or similar), experience integrating them into team workflows (CI, code review, documentation), and mature judgment about verification, security, and the limits of AI-generated code.
- Familiar with Agile/Scrum methodologies. You're comfortable working in iterative delivery cycles, participating in sprint planning, and adapting quickly to changing priorities.
- Influential communicator. Excellent written and verbal communication with technical and business audiences alike, with sharp analytical and troubleshooting instincts. You align engineers, product partners, and stakeholders without direct authority, make crisp calls, and bring people with you.
- Bias for ownership. You have an extreme ownership mindset and will wear any hat required to get the job done. You thrive in a fast-moving, self-directed environment: you set the bar, hold it, and take full accountability for outcomes.
- Passion for collecting is a plus.
