• Developing backend tasks in .NET, maintaining APIs, and assisting with system integrations • Writing clean, testable code and creating unit tests with mentor support • Utilizing AI tools consciously for code generation, refactoring, debugging, and technical research • Participating in code reviews, internal workshops, technical demos, and analyzing bugs/logs • Collaborating closely with experienced developers and verifying AI-generated solutions before implementation

• Own core exchange backend systems. Take deep ownership of the systems that power order matching, position management, and order state across perpetual futures. • Build and optimize the matching engine. Profile hot paths, reduce lock contention, and drive improvements to latency and throughput backed by benchmarks and production-grade evidence. • Design for correctness at scale. Build queueing and backpressure mechanisms that keep the orderbook consistent and reliable under high load, without crossing books, creating ghost fills, or introducing stale order state. • Build for observability. Instrument systems with metrics, logs, and traces. Establish rollback guarantees and narrow critical paths that make production incidents faster to detect, diagnose, and resolve. • Improve runtime performance. Optimize CPU, memory, and allocation behavior to meet the demands of a high-throughput exchange in production. • Own production incidents end-to-end. Debug latency and correctness issues directly, and drive follow-through that leaves the system measurably better each time.

• Plan, conduct, and coordinate Agile software development activities, focusing on the maintenance and sustainment of NCEI’s enterprise archive systems • Apply the appropriate standards, processes, procedures, and tools throughout the software development lifecycle • Manage the lifecycle of change requests, deploying changes across various environments • Ensure software standards are met • Correct program errors, prepare operating instructions, compile documentation of program development, and analyze system capabilities to resolve questions of program intent, output requirements, input data acquisition, programming techniques, and controls

• Own outcomes end-to-end, wherever you're deployed. • Frame the problem, design it, ship it, own the metric. • Descope to the cheapest version that works. • Prototype in days. • Stay close to customers. • Shadow sales and support, join advisory conversations, and let customer reality drive technical decisions. • Propose what to build, defer, and kill. • Own your domain and share it. Its interfaces, reliability, performance, and security, including on-call for the systems you own. • Consent and PII handling are central to customer trust. • Bring patterns, tooling, and hard-won context back to the wider org rather than hoarding them. • Build and scale both the product surface and the platform beneath it. • Ship responsive, accessible React interfaces with a strong UI/UX eye, back them with Node.js and Python services and APIs, and keep the cloud batch platform and data pipelines that deliver multi-tenant products reliable as they scale. • Architect AI-driven leverage. • Build AI-assisted workflows that hold up, evolve the team's practices as tooling changes (Cursor, Claude Code, and whatever comes next), and look for product wins AI unlocks (smarter routing, anomaly detection, and more). • Multiply the teams you touch. • Mentor on judgment and product sense, not just syntax. • Write decision memos and ADRs. • Enable Product and Design to self-serve.