• Develop, implement, and deploy scalable, efficient, and reusable front-end and back-end solutions. • Build performant front-end applications that consume and present large volumes of data. • Write clean, maintainable, and well-documented code. • Refactor existing services for a sustainable future. • Collaborate with fellow engineers, product managers, and stakeholders to ensure we're building the right features, in the right way, to advance the platform. • Improve application performance through caching, optimization, and efficient data delivery strategies.

• Build and evolve the MCP server and agent tool surfaces that AI clients use to query Endgame's context graph — designing tool schemas, managing context windows, and making sure every tool call returns the right data fast. • Ship the frontend features, tRPC routes, and React components that customers interact with daily — from admin dashboards to the core product UI. • Design and harden the public API layer: authentication, Redis-backed rate limiting, quota enforcement, and the observability to know when something goes wrong. • Wire new data sources into the product by integrating with the context graph and making that data available through both human-facing UI and agent-facing tool surfaces. • Translate ambiguous product goals into concrete engineering scope, then ship it. You'll work closely with product, design, and the rest of engineering, and you'll often be the person turning a customer pain point into a novel solution. • Own end-to-end quality on the surfaces you ship: performance, reliability, security, observability. AI raises the floor, your taste raises the ceiling.

• Design and champion internal Quality Programmes: Drive engineering-wide process changes and ensure pods adopt new reliability standards without friction. You will operate with a product-owner mindset to define and evangelize the internal reliability roadmap. • Define and enforce hard metrics: Measure and establish hard quality telemetry (e.g., DORA metrics, bug-to-deploy ratios, SLIs/SLOs) to create a quantifiable baseline for platform reliability. • Architect Shift-Left Pipeline Gates: Weave automated Performance, Security, and Accessibility checks directly into the CI/CD pipeline at the PR level in tight partnership with Eng Core. • Build Real-World Load Testing: Shift load and performance testing left for every customer onboarding, validating real-world assumptions. • Engineer Synthetic Data & Production Canaries: Build the architecture for safe synthetic data injection to unblock heavy load-testing and live-production canaries, strictly isolating test data from authentic user telemetry. • Leverage Generative AI Tooling: Actively utilize AI assistants (e.g., Gemini, Claude, Cursor, Codex) to accelerate the development of testing frameworks, automate infrastructure code, and design advanced testing architectures. • Drive Tooling Consolidation: Lead the technical migration away from expensive, legacy testing infrastructure to a unified, AI-supported automation stack. • Help define and maintain development practices: Enable fast iteration while ensuring quality, including writing tests and documenting key implementations.

Role Description Join the Global Cloud Services organization's FinOps Tools team, which is building ServiceNow's next-generation analytics and financial governance platform. Our team owns the full modern data stack: Trino for distributed queries, dbt for transformations, Iceberg for lakehouse architecture, Lightdash for business intelligence, and Argo Workflows for orchestration. You will own the Forecast Engine, the system that turns ServiceNow's cloud capacity and cost actuals into forward-looking forecasts, then automatically tracks those forecasts against plan and budget and alerts the right people when reality diverges. The Forecast Engine also feeds directly into our Future Capacity Reservation (FCR) automation: its forecast of fleet growth and workload migration timing is the signal that drives how much hyperscaler capacity to reserve, in which providers and regions, and when, against the lead-time windows FinOps and Cloud Operations plan around. The Forecast Engine is the simulation and automation core behind FinOps capacity and cost planning. It reads forecasting actuals from the lakehouse and runs a deterministic multi-period simulation of fleet growth, workload migration, placement, and sizing. It validates each result against hard invariants and publishes forecasts that data scientists, analysts, and FinOps engineers consume in Lightdash. As our Staff Software Engineer for the Forecast Engine, you will design and build the automation layer around the engine: - Scheduled forecast runs - Variance and budget tracking against plan - Anomaly and threshold alerting - First-class integration with planning systems, Splunk, and the broader observability stack - The handoff that turns forecasts into Future Capacity Reservation (FCR) recommendations This role demands speed and high velocity. You will take a proven simulation core and rapidly make it a dependable, observable, self-monitoring product that the organization plans against, shipping working increments fast and iterating in tight loops. The automation layer around the engine is greenfield: you will build it from the ground up. We operate like a small startup, and this is the operating mode of the role and the department: we move quickly, deliver early, keep process light, and keep momentum. Qualifications - Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. - 8+ years of experience in software engineering, with a track record of delivering high-quality products with deep expertise in backend systems and cloud-native, data-intensive architecture with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience in Computer Science, Engineering, or related technical field; or equivalent experience. - Strong skills in a systems or backend language (Rust, Go, Java, C++, or similar) and in Python for data tooling, automation, and analysis. - Proven track record building automated, scheduled data or forecasting pipelines that run reliably in production. - Demonstrated ability to deliver at high velocity: shipping production-quality software fast, in tight iteration loops, without sacrificing reliability. - Proven track record of greenfield development and building from scratch in environments with evolving requirements. - Hands-on experience building variance/anomaly detection, budget or SLA tracking, or alerting systems at scale. - Experience integrating with observability and logging platforms (Splunk, Datadog, Prometheus/Grafana, or similar). - Experience with workflow orchestration systems (Argo, Airflow, or similar) and with the modern data stack. - Strong knowledge of data structures, algorithms, object-oriented and data-oriented design, design patterns, and performance optimization. - Familiarity with automated testing frameworks and integrating tests into CI/CD pipelines. - Understanding of software quality principles including reliability, determinism, observability, and production readiness. - Ability to troubleshoot complex systems and optimize performance and memory across the stack. - Experience validating data correctness: reconciling pipeline outputs against ground-truth actuals and catching silent regressions. - Comfort with development tools such as IDEs, debuggers, profilers, source control, and Unix-based systems. - Full professional proficiency in English. Requirements - Forecasting & simulation: time-series or simulation-based forecasting, scenario modeling, and reconciliation of forecasts against actuals. - Variance & alerting: budget vs. actual tracking, anomaly/threshold detection, alert routing, and noise control (deduplication, suppression, severity). - Observability: Splunk (search, dashboards, alerts) and metrics/logging integration for pipeline and forecast health. - Orchestration: Argo Workflows or similar: scheduled runs, retries, idempotency, failure alerting. - Modern data stack: Trino, dbt, Iceberg, Lightdash, or similar lakehouse and BI technologies. - Systems engineering: streaming/bounded-memory data processing, deterministic and reproducible computation, and config-driven design (no hardcoded business constants). - Data contracts & quality: fail-loud ingestion, upstream contract views, and correctness invariants enforced in code. - API & integration design: RESTful services, authentication (OAuth/SAML), and webhook/notification integrations. Benefits - Base pay of $166,500 - $291,400, plus equity (when applicable), variable/incentive compensation and benefits. - Health plans, including flexible spending accounts. - 401(k) Plan with company match. - ESPP and matching donations. - Flexible time away plan and family leave programs.