Note: The job is a remote job and is open to candidates in USA. ServiceNow is an AI control tower for business reinvention, helping organizations work smarter and faster. The Staff Software Engineer for the Forecast Engine will design and build automation layers for forecasting, ensuring accurate financial governance and capacity planning.
Responsibilities
- Design and develop scalable, maintainable, and reusable software components with a strong emphasis on performance, determinism, and reliability
- Collaborate with product managers and FinOps partners to translate planning and budgeting requirements into well-architected solutions, owning features from design through delivery
- Build intuitive and extensible interfaces for forecast consumption (Lightdash models, alert payloads, and APIs) ensuring flexibility for finance and capacity-planning use cases
- Contribute to the design and implementation of new Forecast Engine capabilities while enhancing existing simulation, validation, and publish paths
- Integrate automated testing into development workflows to ensure consistent quality across releases, including determinism (byte-identical output) and forecast-accuracy regression checks
- Participate in design and code reviews ensuring best practices in performance, maintainability, and testability
- Develop comprehensive test strategies covering functional, regression, integration, and accuracy aspects (period-over-period identity, backtest grading against real actuals)
- Foster a culture of continuous learning and improvement by sharing best practices in engineering and quality
- Promote a culture of engineering craftsmanship, knowledge-sharing, and thoughtful quality practices across the team
- Own the architecture of the Forecast Engine and the automation layer around it: scheduled runs, variance/budget tracking, and alerting
- Lead technical decision-making on forecast cadence, reconciliation against actuals, alert routing, and the contract between the simulation core and downstream consumers
- Establish best practices for forecast automation: idempotent scheduled runs, deterministic reproducibility, fail-loud data contracts, and no silent fallbacks
- Define how forecast signals (variance, budget breach, capacity headroom, migration drift) are computed, thresholded, and surfaced
- Drive innovation in forecasting and planning automation, including the responsible use of AI/ML tooling to accelerate development and analysis
- Build the automation that runs the Forecast Engine on a schedule via Argo Workflows, with retries, alerting on failure, and run-to-run reproducibility
- Develop variance and budget tracking: reconcile each forecast against plan and against the latest actuals, compute deltas at the grains that matter (provider, region, pod, workload), and persist a queryable variance history
- Implement alerting that fires on budget breach, forecast drift, capacity thresholds, and pipeline health, routed to Splunk and the team's notification channels
- Integrate with planning systems so plan/budget targets flow into the engine and forecast outputs flow back out to the planning surface
- Drive the Future Capacity Reservation (FCR) handoff: translate the forecast of fleet growth and migration timing into reservation recommendations (how much capacity, which providers/regions/pods, and by when), aligned to hyperscaler procurement lead-time windows and reconciled with Cloud Operations so the same capacity is never reserved twice
- Build and extend the Rust simulation core (period loop, growth, migration, routing, packing, sizing, validation) and its streaming Trino read and Iceberg publish paths
- Create and maintain the Lightdash forecast and variance marts (standard dbt models on the published tables) that finance and capacity partners consume
- Design the forecast data contract (the upstream view the engine reads) so data-quality problems halt loudly and are fixed at the source, never papered over downstream
- Implement scheduled, observable forecast runs with full run lineage: inputs, seed, config, output location, and metrics for every run
- Build observability and monitoring for the Forecast Engine: run success rates, forecast latency, memory ceilings, accuracy drift, and alert-delivery health, emitted to Splunk and the observability stack
- Establish an automation foundation that scales from a handful of scheduled scenarios to a broad, multi-scenario forecasting program
- Create scheduled, parameterized forecast scenarios with opinionated structure: pinned config, deterministic seeds, validated inputs, and published outputs
- Build tooling for one-command scenario runs and for promoting a scenario from ad-hoc to scheduled with minimal manual intervention
- Establish guardrails: input data contracts, resource/memory ceilings, and loud halts that surface real problems instead of producing wrong-but-quiet numbers
- Collaborate closely with FinOps analysts and capacity planners to rapidly iterate on variance definitions, alert thresholds, and the signals that matter, without over-engineering
- Prioritize forecast reliability, accuracy tracking, and clear alerting over feature breadth
- Use modern AI development tools (e.g., Claude Code, Cursor, GitHub Copilot) to accelerate development, testing, and analysis, and help the team adopt effective, well-validated AI-assisted practices
- Work autonomously with guidance from Engineering and FinOps leadership
- Collaborate with DevOps and platform teams on scheduling infrastructure, CI/CD pipelines, and Splunk/observability integration
- Partner with FinOps Tools team members working on Trino, dbt, Lightdash, and Iceberg to ensure seamless integrations
- Partner with finance and capacity-planning stakeholders to ensure forecasts, variance, and alerts map to how they actually plan and budget
Skills
- Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry
- 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. We operate like a small startup, and this role thrives on that: short paths from idea to shipped, minimal process, and high ownership
- 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
- 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
- Equity (when applicable)
- Variable/incentive compensation
- Health plans, including flexible spending accounts
- A 401(k) Plan with company match
- ESPP
- Matching donations
- A flexible time away plan
- Family leave programs
Company Overview
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