Product Manager For AI-Ready Data Quality PlatformAt F5, we strive to bring a better digital world to life. Our teams empower organizations across the globe to create, secure, and run applications that enhance how we experience our evolving digital world. We are passionate about cybersecurity, from protecting consumers from fraud to enabling companies to focus on innovation.Everything we do centers around people.
That means we obsess over how to make the lives of our customers, and their customers, better. And it means we prioritize a diverse F5 community where each individual can thrive.We are building an AI-native enterprise, and high-fidelity data is the substrate.We are looking for a technically fluent Product Manager to architect and scale an AI-Ready Data Quality Platform built on Databricks and Unity Catalog.This is not a traditional MDM or stewardship role.You will define and ship the platform capabilities that make our AI Data Fabric trustworthy, observable, and production-grade — from real-time anomaly detection to CI/CD-native schema enforcement to automated data contract validation.If you think of data quality as code, treat governance as infrastructure, and believe AI systems are only as good as the data feeding them — this role is for you.What You'll OwnBuild the AI-Ready Data Quality PlatformOperationalize Data Quality in the AI Data FabricDrive Data Ownership as a Product DisciplineAI + Governance ConvergenceWhat You Bring5+ years in Product Management for Data Platforms, Analytics, or AI InfrastructureDeep working knowledge of:Databricks Lakehouse architectureUnity Catalog governance constructsdbt transformation workflowsCI/CD patterns for data pipelinesData observability and monitoring patternsStrong SQL fluency and comfort reading Python/Scala data pipeline codeExperience defining data contracts and schema evolution strategiesUnderstanding of streaming frameworks (Kafka, Spark Structured Streaming, etc.)Experience supporting AI/ML workloads in production environmentsBonus:Experience with modern data observability platforms (Monte Carlo, Bigeye, etc.)Familiarity with feature stores and model lifecycle toolingKnowledge of domain-oriented data mesh architecturesHow We Measure Success% of AI datasets certified as "production-grade"Reduction in downstream model failures due to data issuesAutomated anomaly detection coverage across critical pipelinesAdoption of data ownership model across service domainsCI/CD-integrated data validation coverageWhy This Role MattersAI systems amplify whatever data they are fed.This role ensures:We trust our data.Our models are reproducible.Governance is automated.Quality is engineered, not inspected.You won't be managing spreadsheets of bad records.You will be building the infrastructure that makes AI reliable at scale.