Senior Technical Product Owner - FinOps Data

Salary: $190,900 - 334,100 per year Requirements:

• We need 12+ years of experience in technical product ownership, technical program management, or product management for data platforms, data engineering, or infrastructure, with a record of delivering data products that internal engineering teams actively use.
• We require a bachelors degree, or 10 years of experience with a masters degree, or a PhD with 7 years of experience in Computer Science, Engineering, or a related technical discipline, or equivalent experience.
• We look for experience thinking about or applying AI in workflows, decision-making, or problem-solving.
• We need proven ownership of internal platforms or data infrastructure as a product, including defining SLAs, measuring adoption, and improving based on user feedback.
• We value the ability to turn ambiguous input from multiple stakeholder groups into clear, sequenced engineering specifications.
• We need experience partnering closely with engineering managers and influencing roadmap and priorities without direct authority.
• We expect strong fluency in data engineering concepts, including ELT/ETL pipelines, data lake architecture, data quality, source onboarding, and data governance implementation.
• We expect strong fluency in platform engineering concepts, including distributed query engines, developer environments, BI tools, and the operational realities of open-source infrastructure.
• We need the ability to lead through influence across teams, set product direction, and raise the quality bar.
• We require excellent stakeholder management across engineering, data, finance, and executive audiences, plus strong technical writing and documentation skills.
• We require full professional proficiency in English.
• We look for technical fluency in modern data stack technologies such as Iceberg, Hive, Parquet, CDC, streaming ingestion, Trino, Presto, Coder, JupyterHub, Lightdash, Redash, Hive Metastore, Nessie, dbt, and Argo Workflows.
• We expect a solid understanding of data governance concepts, including classification, access control, lineage, retention, and audit logging.
• We value familiarity with developer experience challenges, including workspace tooling, CI/CD for data pipelines, notebook workflows, and query development.
• We need strong product judgment, the ability to prioritize decisively, say no clearly, and negotiate timelines without harming relationships.
• We need the ability to communicate effectively with both technical and non-technical audiences and to define and improve success metrics for internal products.
• Nice-to-have experience includes Trino, dbt, Apache Iceberg, Coder, JupyterHub, FinOps, cloud cost management, financial data platforms, governance/compliance translation work, Cloudera/Hadoop migration planning, Project Nessie, and developer experience metrics.

• We own the product vision and roadmap for our FinOps data engineering and platform services capabilities.
• We define what production-ready means for our pipelines and platform services, including SLAs for freshness, reliability, and availability.
• We sequence roadmap work so platform capabilities are in place before dependent pipeline and analytics efforts.
• We gate roadmap phases on readiness criteria rather than arbitrary dates.
• We own requirements and prioritization for ELT pipeline development, including source onboarding order and acceptance criteria.
• We specify data contracts, quality standards, and freshness SLAs for each pipeline.
• We drive fast, repeatable source onboarding from raw source to governed lakehouse table.
• We translate governance requirements into concrete engineering specifications and acceptance criteria.
• We measure pipeline reliability and use those insights to prioritize reliability improvements when needed.
• We own requirements and prioritization for platform services together with the platform services manager.
• We define adoption and satisfaction goals for platform tools and improve user experience, self-service, and developer productivity.
• We advocate for better developer workflows across workspaces, CI/CD, notebooks, and query tools.
• We guide onboarding for new users and use cases, ensuring documentation, access setup, and initial support are smooth.
• We serve as the main intake point for requests from FinOps practitioners, finance, capacity planning, and engineering teams.
• We convert broad requests into sequenced, shippable work with clear acceptance criteria.
• We communicate roadmap progress, trade-offs, and dependencies in a way stakeholders can act on.
• We partner with the Data Engineering team on the Cloudera-to-lakehouse migration, jointly managing wave sequencing, readiness criteria, and stakeholder updates.
• We coordinate with the Principal Engineer on migration architecture while driving day-to-day prioritization and execution planning with Data Engineering leadership.
• We align with the Analytics Engineering TPO to ensure downstream modeling and dashboard needs are supported.
• We translate enterprise governance policies into actionable work for engineering teams.
• We ensure data classification, access control, lineage tracking, and audit requirements are implemented across pipelines and platform services.
• We track governance compliance across the data estate and surface gaps that need engineering attention.
• We apply AI and ML tools where they can accelerate pipeline development, platform operations, source onboarding, or data quality monitoring.
• We identify opportunities to make data discovery and platform usage more self-service through automation and intelligent tooling.

• AI
• AWS
• Azure
• CI/CD
• Cloud
• ETL
• Hadoop
• Hive
• Support
• ServiceNow
• dbt
• Embedded
• Jupyter
• Product Owner
• RBAC
• Security

More:

We are ServiceNow, and our AI platform helps organizations bring any AI, any data, and any workflow together so people can work smarter, faster, and better. Our FinOps Platform Engineering organization within Global Cloud Services builds our internal FinOps Control Tower for cloud cost governance, financial accountability, and unit economics across AWS, Azure, and Google Cloud Platform. This role sits inside a collaborative engineering environment focused on a modern open-source stack and supports teams working on the data lake, ELT pipelines, and platform services that power analytics at scale. We offer a base salary range of $190,900 to $334,100 for this location, plus equity when applicable, variable compensation, and benefits including health plans, flexible spending accounts, a 401(k) with company match, ESPP, matching donations, flexible time away, and family leave programs. We value flexibility, trust, craftsmanship, quality, innovation, continuous learning, and shared success, and we support a distributed work model with flexible, remote, or office-based personas depending on role needs.

last updated 33 week of 2026