SAS's 0-to-1 entry into data cataloging, discovery, governance, lineage, and AI/ML asset management. Joined during customer discovery, drove the platform through GA in 2021, and led scale-out to 100+ deployed enterprise customers across financial services, insurance, healthcare and pharma, public sector and federal civilian, telecom, retail, and technology.
What I owned
- Product strategy and roadmap for a platform spanning discovery, classification, profiling and quality, governance and stewardship, and AI/ML asset cataloging, clustered into five capability pillars on a unified metadata layer.
- v2 evolution from keyword search to embedding-based semantic search paired with a knowledge graph capturing business context, ownership, lineage, and asset relationships.
- Extending cataloging and discovery to model and decision assets, governing them in the same plane as data. Shipped data products and business glossary as first-class authoring surfaces. Integrated with model registries (MLflow, customer-specific) so model lineage flowed into the catalog alongside data.
- A 100+ classifier portfolio across regulatory packs (GDPR, CCPA, HIPAA, PCI-DSS, GLBA, SOX, BCBS 239) and industry packs, with confidence-scored outputs and human-in-the-loop steward workflows tunable per tenant.
- On-prem and air-gap deployable architecture as a strategic moat in regulated verticals. Won deals where SaaS-only competitors could not bid.
- Pricing and packaging redesign post-GA: term license tiers and capability-based bundles aligned with how customers deployed and expanded.
- Integration coverage across 40+ connectors (Snowflake, Databricks/Unity, Microsoft Fabric/Synapse, BigQuery, Teradata, Oracle, S3/ADLS, SharePoint, Kafka, dbt, Airflow, Salesforce). Defined customer-pull and vertical-coverage priorities for the connector roadmap in partnership with the connector platform PM.
- The analyst relations program: 10+ Gartner and Forrester briefings on AI and data governance positioning.
How the team was structured
Worked with 12+ design partners pre-GA across financial services, healthcare, public sector, pharma, and telecom. Partnered with a 36-person product org (30 engineering, 4 design, 2 data science) plus PMM, security, legal, and finance, reporting to the GM/Business Unit Lead. Proposed a hub-and-spoke PM structure that was partially adopted, with module-level PM peers picking up capability pillars.
What this demonstrates
0-to-1 platform PM at enterprise scale. Embedding-based semantic search and knowledge graphs in production, before "AI" was the headline. AI/ML asset governance specifically, a rare specialization. Deep regulated-industry coverage across multiple verticals. Analyst relations and pricing strategy alongside product. Cross-functional leadership at a 36+ person org level.