Senior Staff Technical Product Owner - FinOps Platform Engineering

About the role

The FinOps Platform Engineering team 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).

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.
• 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.
• 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.
• 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.

Qualifications

• 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.
• Strongly preferred 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.
• Nice to have Experience with the ServiceNow platform (administration, development, or Performance Analytics).
• 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).