Principal Product Manager - AI Data Quality

At 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. The Mission   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   Own   Build the AI-Ready Data Quality Platform   Define and ship native data quality capabilities inside   Databricks Lakehouse   Productize policies and controls within   Unity Catalog   (lineage, access, schema enforcement)   Embed data contracts and validation logic directly into pipelines   Partner with data engineering to integrate   dbt -based transformation layers into quality frameworks   Drive metadata, lineage, and semantic standardization as first-class platform features   Operationalize Data Quality in the AI Data Fabric   Design real-time anomaly detection systems (statistical + ML-driven)   Build upstream schema validation into CI/CD workflows (shift-left quality)   Define SLOs/SLAs for data products   Enable automated drift detection for training and inference datasets   Implement observability across streaming and batch architectures   You will treat data quality like SRE treats uptime.   Drive Data Ownership as a Product Discipline   Establish a   data product ownership model   across service teams   Define what “production-grade data” means for AI use cases   Build self-service tooling for teams to   monitor   and certify their data   Incentivize measurable quality accountability at the domain level   This role transforms culture by building the platform that enforces it.   AI + Governance Convergence   Define how governed datasets become AI-ready assets   Enable traceability from raw source → curated feature sets → model inputs   Align catalog metadata with AI feature stores and inference pipelines   Partner with ML teams to support model reproducibility and dataset versioning   What You Bring   5+ years in Product Management for Data Platforms, Analytics, or AI Infrastructure   Deep working knowledge of:   Databricks Lakehouse architecture   Unity Catalog governance constructs   dbt   transformation workflows   CI/CD patterns for data pipelines   Data observability and monitoring patterns   Strong SQL fluency and comfort reading Python/Scala data pipeline code   Experience defining data contracts and schema evolution strategies   Understanding of streaming frameworks (Kafka, Spark Structured Streaming, etc.)   Experience supporting AI/ML workloads in production environments   Bonus:   Experience with modern data observability platforms (Monte Carlo, Bigeye, etc.)   Familiarity with feature stores and model lifecycle tooling   Knowledge of domain-oriented data mesh   architectures       How We Measure Success   % of AI datasets certified as “production-grade”   Reduction in downstream model failures due to data issues   Automated anomaly detection coverage across critical pipelines   Adoption of data ownership model across service domains   CI/CD-integrated data validation coverage   Why This Role Matters   AI 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.   #LI-JB1 The Job Description is intended to be a general representation of the responsibilities and requirements of the job. However, the description may not be all-inclusive, and responsibilities and requirements are subject to change. The annual base pay for this position is: $156,800.00 - $235,200.00 F5 maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, geographic locations, and market conditions, as well as to reflect F5’s differing products, industries, and lines of business. The pay range referenced is as of the time of the job posting and is subject to change. You may also be offered incentive compensation, bonus, restricted stock units, and benefits. More details about F5’s benefits can be found at the following link:  https://www.f5.com/company/careers/benefits . F5 reserves the right to change or terminate any benefit plan without notice.  Please note that F5 only contacts candidates through F5 email address (ending with @f5.com) or auto email notification from Workday (ending with f5.com or @myworkday.com ) . Equal Employment Opportunity It is the policy of F5 to provide equal employment opportunities to all employees and employment applicants without regard to unlawful considerations of race, religion, color, national origin, sex, sexual orientation, gender identity or expression, age, sensory, physical, or mental disability