Hybrid Principal Product Manager – AI Data Quality

at F5

• Job title: Principal Product Manager – AI Data Quality
• Job type: Full Time
• Experience level: Lead
• Salary: $156,800 - $235,200 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Kafka, Python, Scala, Spark, SQL, Unity
• Location requirements: Hybrid, Seattle, United States

About the role

• Principal Product Manager building AI-Ready Data Quality Platform at F5. Leading data quality capabilities and ensuring data governance in AI systems.

Responsibilities

• 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
• 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
• 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

Requirements

• 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

Benefits

• incentive compensation
• bonus
• restricted stock units
• benefits