Principal Product Manager - AI Data Quality

Product Manager For AI-Ready Data Quality Platform

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.

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
• Operationalize Data Quality in the AI Data Fabric
• Drive Data Ownership as a Product Discipline
• AI + Governance Convergence

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.