Staff Software Engineer, ServiceNow
Forecast Engine
$166.5-291.4k
+ Equity (when applicable) & variable/incentive compensation
Python
Java
Go
Airflow
Splunk
C++
Rust
Prometheus
Grafana
REST API
dbt
Datadog
Mid, Senior and Expert level
San Francisco Bay Area
Remote from Canada, US
Cloud-based digital workflows for enterprises
Be an early applicant
Cloud-based digital workflows for enterprises
1001+ employees
B2BEnterpriseInternal toolsProductivitySaaSCloud Computing
Be an early applicant
$166.5-291.4k
+ Equity (when applicable) & variable/incentive compensation
Python
Java
Go
Airflow
Splunk
C++
Rust
Prometheus
Grafana
REST API
dbt
Datadog
Mid, Senior and Expert level
San Francisco Bay Area
Remote from Canada, US
1001+ employees
B2BEnterpriseInternal toolsProductivitySaaSCloud Computing
Company mission
To transform IT to revolutionize the enterprise.
Role
Who you are
- 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
What the job involves
- 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
- Today it is a fast, single-binary Rust core with a streaming Trino read and an Iceberg publish path
- The next chapter is to turn that engine into an automated, always-on forecasting service
- 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, and the handoff that turns forecasts into Future Capacity Reservation (FCR) recommendations
- You will make the forecast a living signal: recomputed on a cadence, reconciled against actuals, and translated into the capacity reservations that keep hyperscaler supply ahead of demand
- 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
- 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
- Technical Leadership & Architecture
- 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
- Hands-On Development
- 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
- Platform Foundation
- 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
- Forecast Automation & Alerting
- 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
- AI-Augmented Development
- 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
- Collaboration & Integration
- 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
Our take
ServiceNow provides cloud-based solutions that define, structure, manage, and automate services for enterprise operations, transforming old, manual ways of working into modern digital workflows. The company was founded in 2004 with a vision to build a cloud-based platform that would enable regular people to route work effectively through the enterprise.
Much as Salesforce enables a company to manage its external clients by keeping a record of all contacts and interactions, ServiceNow promises an internal system to meet the needs of employees. It has succeeded in claiming half the market, setting itself apart from legacy IT service-management software players like BMC Software, Hewlett Packard Enterprise, Cherwell Software and CA Technologies.
The company's success comes from two product traits: simplicity and customisability. ServiceNow's IT tools don't require the IT department to set them up. Once running, they offer a single collection centre for requests, data points and checklists, all of which can in turn be analysed by algorithms to predict needs, flag concerns and measure efficiency.
The company has more than 7,700 customers, including 85% of the Fortune 500 list of the United States' largest corporations. ServiceNow now looks to increase its revenue threefold in the upcoming years and has entered into a 5-year partnership plan with Visa, beginning with the launch of a Visa-integrated dispute resolution solution for issuers. The company is prioritising organic growth over mergers and acquisitions, with a target revenue of $15B, something which its Visa partnership will no doubt aid in achieving.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Top investors
16% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
Mar 2012
$11m
GROWTH EQUITY VC
Feb 2012
$17.9m
GROWTH EQUITY VC
Total funding: $83.8m
Company benefits
- Generous family leave
- Matched donations
- Annual learning stipends
- Flexible PTO
- Competitive retirement plan
- Paid volunteer time
Company HQ
Santa Clara, CA
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 107 more jobs at ServiceNow