Staff Software Engineer

Collaborative Solutions•Santa Ana, CA

About The Position

Collectors is seeking two Staff Software Engineers for its API and Collectibles Data Team. The team turns the company’s collectibles data into products, including external APIs used by partners and customers and specification data identifying collectibles that Collectors grades, authenticates, and sells. The team owns external-facing APIs and the API gateway layer, developer portals, specification data models and ingestion pipelines, and integrations used by downstream products. Its work supports PSA, Beckett, SGC, Card Ladder, marketplace and research teams, and external partners across millions of collectible specifications and certificates. The Staff Software Engineer will set technical direction and own architecture decisions, API strategy, and quality standards across the team’s portfolio. The role calls for expertise in the team’s services and data flows, as well as a working understanding of adjacent systems across grading, marketplace, vault, identity, CRM, and data/ETL. The position has broad organizational influence because products that display or price collectibles depend on the APIs and data the team delivers. The role includes defining API standards and strategy; evolving the collectibles data platform; improving reliability, performance, and quality; and leading complex, multi-quarter projects. The engineer will work across product and engineering functions, mentor other engineers, promote documentation and knowledge sharing, and establish responsible standards for AI coding tools and workflows. The company values onsite collaboration, and candidates within a one-hour commute of the office may be expected to work onsite most of the time. Further details will be discussed during recruiting.

Requirements

At least 8 years of engineering experience, including technical leadership of multi-engineer, multi-quarter projects.
Track record of designing, shipping, and scaling products with measurable business impact at a high-growth company or scaled consumer platform.
Expert-level backend engineering experience in Java and Python, including strong proficiency with Java Spring Boot.
Experience owning complex API surfaces, asynchronous patterns, and data consistency in distributed systems.
Experience designing and operating REST APIs at scale; GraphQL or gRPC experience where relevant.
Strong judgment in API versioning, backward compatibility, authentication, and developer experience.
Deep relational and NoSQL database and data-modeling expertise, including schema design, migrations, query performance, and shared-database coupling; examples include PostgreSQL and DynamoDB.
Strong distributed-systems fundamentals, including service boundaries, contract design, Kafka-based event-driven architectures, messaging, idempotency, and failure handling in production.
Production experience with AWS and Kubernetes, and infrastructure-as-code fluency with CDK and/or Terraform.
Enough hands-on experience with modern frontend frameworks such as React to design and review across application layers.
Track record of raising engineering standards in testing, observability, documentation, and incident response.
Observability-first practice, including instrumentation, dashboards, alerting, and distributed tracing using Datadog or similar tools.
Daily familiarity with AI coding assistants and agentic tools such as Claude Code, Copilot, or Cursor, and experience integrating them into team workflows.
Mature judgment regarding verification, security, and the limitations of AI-generated code.
Familiarity with Agile/Scrum methodologies, iterative delivery, sprint planning, and adapting to changing priorities.
Excellent written and verbal communication with technical and business audiences.
Strong analytical and troubleshooting instincts and ability to align engineers, product partners, and stakeholders without direct authority.
Ownership mindset, accountability for outcomes, and ability to work in a fast-moving, self-directed environment.

Nice To Haves

Experience with data ingestion and enrichment pipelines.
Experience with API gateways such as Kong.
Passion for collecting.

Responsibilities

Own the end-to-end architecture of the API and data domain, including services, event-driven systems, data stores, and consumers.
Design clean service boundaries, contracts, clear data ownership, and migration paths that support autonomous development and future scaling.
Define API standards and strategy for design, versioning, authentication, rate limiting, and documentation.
Lead API initiatives, including a certificate lookup API and business admin console, a self-service developer portal, migration of legacy public API users, and API gateway consolidation with consistent rate limiting and bot protection.
Design and evolve collectibles data models, pipelines, and access patterns to keep data accurate, consistent, and fast to query.
Lead data initiatives including quality scoring across collectible categories, unifying overlapping specification databases, and exposing unified data through a specification API.
Set standards for testing, observability, performance, and incident response, and use operational data to improve stability and latency.
Lead moderate-to-significant projects spanning one to two quarters, coordinating engineers, stakeholders, and dependencies for predictable delivery.
Plan work using an explicit value-versus-cost framework and communicate blockers and delays proactively.
Translate business requirements into technical solutions and collaborate with Product, Marketplace, Grading, Vault, Data, and platform teams.
Develop a working understanding of adjacent CRM, identity, and data/ETL systems to identify cross-system risks.
Mentor senior and mid-level engineers through design reviews, pairing, and feedback.
Foster knowledge sharing and documentation by facilitating discussions about complex issues with engineers and business stakeholders.
Establish standards and guardrails for AI coding tools, including code review, testing, security, and intellectual-property practices.
Identify effective uses of AI agents, share proven workflows, and coach engineers on the benefits and limitations of AI tools.