Senior Staff Technical Product Owner - FinOps Platform Engineering

ServiceNow•Santa Clara, CA

8h•$190,900 - $334,100•Hybrid

About The Position

Join the FinOps Platform Engineering organization, the team that builds the FinOps Control Tower, ServiceNow's internal platform for cloud cost governance, financial accountability, and unit economics across our multi-cloud estate (AWS, Azure, and GCP). We own the data engineering and business intelligence layers that turn raw multi-cloud billing data into attributed, governed, decision-ready cost information for the people who run ServiceNow's cloud. Our users are FinOps practitioners and the engineering, finance, and executive stakeholders they serve. The team brings together data engineers, platform engineers, BI/reporting engineers, and FinOps domain experts. We partner closely with the FinOps practice, who own the FinOps methodology and business strategy, while we own how the platform delivers against it: the data models, the allocation logic, the semantic layer, and the reporting experiences on top. Our key partners span Finance / FP&A, Public Cloud Engineering, AI Platform Engineering, Capacity Planning Engineering, Migration Engineering, Data Engineering, Data Platform, Data Governance, BI / Analytics Engineering, and CMDB / Metadata Management. As Sr Staff Technical Product Owner for FinOps Platform Engineering, you own the product direction for the FinOps Control Tower's cost-and-usage capabilities, with Showback as the flagship and the roadmap extending into allocation maturity, unit economics, and optimization. This is a hands-on technical product role embedded in engineering, not a market-facing product management role and not a people-management role. You will own the backlog, translate business requirements into concrete data-product and reporting specifications, sequence the roadmap, and drive adoption. You will set direction for the data engineering and BI teams and direct the technical work (dbt models, SQL transformations, semantic-layer definitions, dashboard design) rather than writing it yourself. You lead through influence and deep technical judgment; you will not have direct reports. You'll partner daily with your peer, the FinOps practice lead (who owns the "what should FinOps do"), and own the "how the platform delivers it." You'll also work across Data Governance, the data platform team, and your own data engineers and BI developers. A defining part of the mandate: modernizing how we deliver insight. Our platform runs on a modern data stack (Trino, dbt, Apache Iceberg, and Lightdash on AWS), and today much of our reporting lives in static dashboards (with a Tableau-to-Lightdash migration underway). You will drive the evolution toward governed, self-service data apps built on the semantic layer: custom cost experiences that inherit our access controls and row-level security by default, keep every metric definition consistent, and let practitioners and their stakeholders answer their own questions without waiting on a rebuild. Dashboards become a product, not a backlog of one-off report requests. This is a unique opportunity to own the cost-intelligence platform for all of ServiceNow's cloud spend, at Fortune 500 scale, with a clear mandate to modernize it.

Requirements

Experience leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving.
12+ years in technical product ownership, technical program management, or product management of data, analytics, or reporting platforms, with a track record of shipping data products that stakeholders actually adopt.
Proven ownership of dashboards, metrics, and reporting as a product (not a backlog of ad hoc report requests), including defining metrics, KPIs, and the reporting experiences executives rely on.
Demonstrated ability to translate ambiguous business requirements into concrete data-product specifications and to sequence a roadmap that ships incrementally.
Experience defining the success metrics for a platform or data product (adoption, coverage, and quality measures) and driving adoption against them, not just delivering features.
Experience directing data engineering and BI work (dbt/SQL transformations, warehouse/data-model design, and semantic-layer definitions), partnering with engineers who build it, without needing to write production code yourself.
Strong working fluency with the modern data stack and semantic-layer concepts (dbt, a cloud data warehouse, a metrics/semantic layer, BI tooling), enough to make and defend product and modeling decisions with engineers.
Proven ability to lead through influence across teams you do not manage, setting technical product direction and raising the bar.
Excellent stakeholder management across engineering, data, finance, and executive audiences, and strong technical writing and documentation skills for both engineering and business readers.
Full professional proficiency in English.

Nice To Haves

Cloud FinOps or cloud cost management experience: cost allocation, showback/chargeback, unit economics, or cloud billing data (AWS, GCP, and/or Azure).
Familiarity with cloud billing datasets and the realities of normalizing cost data across providers.
Experience with the ServiceNow platform (administration, development, or Performance Analytics). Our target users live here.
Experience building or operating a FinOps platform end to end.
Knowledge of unit-economics modeling, forecasting, and variance analysis.
Familiarity with semantic-layer / metrics tools (Lightdash, dbt metrics, or similar) and the emerging data-apps pattern for governed, self-service analytics.
Experience with Net Effective Cost or commitment/discount modeling (RIs, Savings Plans, committed-use discounts, EDP-style contracts).

Responsibilities

Own the product vision and roadmap for the FinOps Control Tower's cost-and-usage capabilities, with Showback as the foundation and a clear path through allocation → unit economics → optimization.
Define Showback as a product: clear attributed spend by stakeholder, a consistent allocation hierarchy (CSP → BU → SKU → instance), and clean separation of production, non-production, trial, and internal workloads, serving BU-finance cost summaries and platform-team instance-level detail from one source of truth.
Drive the modernization from static dashboards to governed data apps on the semantic layer: self-service, access-controlled cost experiences with consistent metric definitions, replacing one-off report requests with a durable data-product surface.
Own requirements intake, prioritization, and the backlog across FinOps practitioners, finance leaders, engineering teams, and executives, turning ambiguous asks into sequenced, shippable work.
Define the data-product and semantic-layer specification: cost metrics (e.g., Net Effective Cost, actual vs. forecast), allocation logic, and the dimensional model (BU, SKU, instance, customer/workload), translating business requirements into work the data engineering and BI teams build in dbt, the warehouse, and the semantic layer.
Deliver unified multi-cloud cost visibility across AWS, GCP, and Azure; partner with engineering to onboard new billing datasets, normalize cost structures, and resolve provider inconsistencies as Azure reaches parity.
Ensure every reporting and data product aligns with FinOps data governance standards: tagging and metadata, cost-attribution rules, SKU-hierarchy consistency, and enterprise data-handling controls.
Own the roadmap beyond reporting, sequencing the platform through clear phases: unified multi-cloud showback → a cost-allocation framework and SKU-level attribution → planning and forecasting inputs → optimization (rightsizing, waste and unattached-resource identification, opportunity sizing) and unit economics. Gate each phase on the data-model and semantic-layer work it depends on.
Define the platform's adoption and coverage targets, then drive to them. Establish the measurement model for how success is quantified (showback coverage of spend, active adoption across teams and personas, and allocation accuracy), set the targets in partnership with the FinOps practice and finance leadership, and use them to steer prioritization. This is about owning the definition of "good," not just reporting against numbers handed to you.
Lead through influence: align the engineers building alongside you to the target direction, review designs, resolve technical tensions, and hold the quality bar, without taking the keyboard away from them.
Apply AI/ML tooling where it accelerates the work and, on the roadmap, where it makes cost insight more self-service and proactive for practitioners.