Senior Staff Technical Product Owner - FinOps Data Platform & Engineering

ServiceNow•Santa Clara, CA

1d•$190,900 - $334,100•Hybrid

About The Position

Join the FinOps Platform Engineering organization within Global Cloud Services (GCS), the team that builds ServiceNow’s internal FinOps Control Tower for cloud cost governance, financial accountability, and unit economics across our multi-cloud estate (AWS, Azure, and GCP). Our organization is structured around three core teams: a Data Engineering and Data Lake team that builds the ELT pipelines landing data into our Iceberg lakehouse and owns data governance; a Platform Services team that operates our open-source platform stack (Trino, Lightdash, Coder, Jupyter, Redash, Hive Metastore/Nessie); and an Analytics Engineering team that builds the dbt data models and supports analysts creating dashboards and data apps. This role supports the first two of those teams. You will be the product voice for the data engineering and platform services workstreams, working as a collaborative peer alongside the engineering leaders who own execution. As Senior Staff Technical Product Owner for FinOps Data Platform & Engineering, you own the product direction for both the data lake and ELT pipeline capabilities and the platform services that data engineers and analysts build on every day. You are the connective tissue between what the organization needs from its data foundation and what the engineering teams build. This is a hands-on technical product role embedded in engineering. You will own the backlogs for two teams, translate requirements from internal users and FinOps stakeholders into concrete specifications, sequence the roadmap, and drive adoption. You will not manage people directly. You lead through influence, deep technical judgment, and close partnership with the engineering managers and senior engineers who own delivery. Your stakeholders span both directions. Inward, you serve the data engineers, platform engineers, and analytics engineers who use the pipelines and platforms daily. Outward, you serve the FinOps practitioners, finance leaders, and capacity planners who depend on data landing reliably and platforms being available when they need them. A defining part of the mandate is developer experience. The people building on this platform (data engineers using Coder workspaces, writing dbt models, running notebooks, querying Trino) are your users too. You will advocate for improvements to their workflows and partner with the platform services manager to make those improvements real. You will also translate data governance requirements from compliance, security, and enterprise governance teams into actionable engineering work. You will not define governance policy, but you will ensure the platform and pipelines implement it correctly and completely. The broader organization is migrating off Cloudera onto the modern lakehouse. You will partner closely with the Data Engineering team on this effort, jointly owning the sequencing of workload migration, defining readiness criteria for each wave, and coordinating stakeholder communication. The Principal Engineer leads the overall migration architecture, but you and the Data Engineering lead will drive the day-to-day prioritization and execution planning together.

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 platforms, data engineering, or infrastructure, with a track record of shipping data products that internal engineering teams actually adopt.
Bachelor’s degree required; or 10 years with a Master’s degree; or a PhD with 7 years of experience in Computer Science, Engineering, or a related technical field; or equivalent experience.
Proven ownership of internal platform or data infrastructure as a product, including defining SLAs, measuring adoption, and driving improvements based on user feedback.
Demonstrated ability to translate ambiguous requirements from multiple stakeholder groups into concrete, sequenced engineering specifications.
Experience working as a collaborative peer with engineering managers, influencing roadmap and priorities without direct authority over the teams.
Strong working fluency with data engineering concepts: ELT/ETL pipelines, data lake architecture, data quality, source onboarding, and data governance implementation.
Strong working fluency with platform engineering concepts: distributed query engines, developer environments, BI tooling, and the operational concerns of running open-source infrastructure.
Proven ability to lead through influence across teams you do not manage, setting product direction and raising the quality bar.
Excellent stakeholder management across engineering, data, finance, and executive audiences, with strong technical writing and documentation skills for both engineering and business readers.
Full professional proficiency in English.
Data engineering and lakehouse. ELT pipeline patterns, data lake architecture (Iceberg, Hive, Parquet), CDC and streaming ingestion concepts, data quality frameworks, and source onboarding at scale.
Platform services. Distributed query engines (Trino, Presto), cloud development environments (Coder, JupyterHub), BI platforms (Lightdash, Redash), and catalog systems (Hive Metastore, Nessie).
Data governance. Data classification, access control (RBAC, row-level security), lineage tracking, retention policies, and audit logging. Enough depth to specify requirements, validate implementations, and identify gaps.
Developer experience. Understanding of how data engineers work day-to-day: workspace tooling, CI/CD for data pipelines, notebook workflows, query development, and the friction points in each.
Modern data stack. dbt, Trino, Apache Iceberg, Argo Workflows, and how they compose into an end-to-end analytics platform. Enough depth to make and defend product and sequencing decisions with engineers.
Observability and SLAs. Pipeline monitoring, freshness tracking, platform health metrics, and SLA design for internal data products.
Proven ability to work as a collaborative peer with engineering leadership, bringing stakeholder context while respecting technical feasibility and team capacity.
Strong product judgment with the ability to prioritize ruthlessly, say no clearly, and negotiate timelines without damaging stakeholder relationships.
Effective communication across technical and non-technical audiences. Able to present a pipeline SLA discussion to an engineering team and a roadmap trade-off to a finance VP in the same week.
Strong technical writing and documentation skills for specifications, roadmaps, architecture decision records, and stakeholder communications.
Track record of defining success metrics for internal products and driving measurable improvement against them.

Nice To Haves

Direct experience with Trino, dbt, Apache Iceberg, or similar modern data stack technologies in a product ownership or engineering role.
Experience with Coder, JupyterHub, or similar developer environment platforms.
Background in FinOps, cloud cost management, or financial data platforms.
Experience translating data governance or compliance requirements into engineering work at scale.
Familiarity with the Cloudera/Hadoop ecosystem and migration planning off legacy data platforms.
Experience with Project Nessie or other versioned/transactional catalog systems.
Experience defining and tracking developer experience metrics for internal platform teams.

Responsibilities

Own the product vision and roadmap for FinOps data engineering (ELT pipelines, data lake, source onboarding) and platform services (Trino, Coder, Lightdash, Jupyter, Redash, Nessie).
Define what “production-ready” means for data pipelines and platform services. Establish SLAs for data freshness, pipeline reliability, and platform availability, and use them to steer prioritization.
Sequence the roadmap across both teams so that platform capabilities land ahead of the pipeline and analytics work that depends on them.
Gate roadmap phases on clear readiness criteria rather than arbitrary timelines. Ensure each phase builds on a proven foundation.
Own requirements and prioritization for ELT pipeline development. Define which sources to onboard, in what order, and what the acceptance criteria are for each.
Specify the data contracts, quality expectations, and freshness SLAs for each pipeline, translating business needs into work the data engineering team can build and validate against.
Drive source onboarding to be fast and repeatable. Work with data engineers to templatize and automate the path from raw source to governed lakehouse table.
Translate data governance requirements (data classification, access control policies, lineage, retention) into concrete engineering specifications and acceptance criteria.
Ensure pipeline reliability is measurable and improving. Define the metrics, track them, and use them to prioritize investment in reliability over new features when needed.
Own requirements and prioritization for platform services alongside the platform services manager. Bring the user and stakeholder perspective; partner on feasibility and sequencing.
Define adoption and satisfaction targets for platform services. Understand how internal users experience Trino, Coder, Lightdash, and Jupyter, and drive improvements that reduce friction and increase self-service capability.
Advocate for developer experience improvements across the data engineering workflow. Identify pain points in how data engineers use workspaces, CI/CD, notebooks, and query tools, and work with the platform team to resolve them.
Drive platform service onboarding for new users and use cases, ensuring documentation, access provisioning, and initial support are smooth.
Serve as the primary intake point for requirements from FinOps practitioners, finance, capacity planning, and engineering teams that need data landed or platform capabilities extended.
Turn ambiguous asks into sequenced, shippable work with clear acceptance criteria. Push back on scope that doesn’t serve the roadmap, and negotiate timelines with stakeholders when needed.
Define the measurement model for success (pipeline SLA attainment, platform uptime, adoption breadth, source onboarding velocity, developer satisfaction) and report against it.
Communicate roadmap status, trade-offs, and dependencies to stakeholders in terms they can act on.
Partner with the Data Engineering team on Cloudera-to-lakehouse migration planning, jointly owning wave sequencing, readiness criteria, and stakeholder communication for pipeline and platform workloads.
Coordinate with the Principal Engineer on overall migration architecture while driving the day-to-day prioritization and execution planning alongside Data Engineering leadership.
Align with the Analytics Engineering TPO (your peer) to ensure the data models and dashboards team has what it needs from the lake and platform layers.
Translate enterprise data governance policies (from compliance, security, and data governance teams) into actionable specifications for the engineering teams.
Ensure data classification, access control, lineage tracking, and audit requirements are implemented in both the pipelines and the platform services.
Track governance compliance across the data estate and flag gaps that need engineering investment.
Apply AI/ML tooling where it accelerates pipeline development, platform operations, source onboarding, or data quality monitoring.
Identify opportunities to make data discovery and platform usage more self-service through automation and intelligent tooling.