Judgment areas where I set direction, investment, and governance across complex global portfolios.

• Data and decision infrastructure

  Unifies fragmented data estates into one decision layer that leadership can trust for investment and reporting calls.

  • Single source of truth across markets and divisions
  • Investment latency and escalation reduction
  • Semantic ownership with data engineering partners

  Representative: Snowflake, Databricks, Looker, Airflow

• AI product strategy and governance

  Scales AI portfolios in regulated markets by designing governance as a product constraint, not a late-stage review gate.

  • Privacy-safe data architecture at global scale
  • Legal and policy as design inputs
  • Portfolio coherence across 13+ AI products

  Representative: Enterprise AI suites, regulated-market launch patterns

• Enterprise platform and integration

  Modernizes martech and enterprise platforms so catalog, identity, and integration decisions support growth without fragmenting ownership.

  • Multi-market platform and catalog architecture
  • Martech and CRM ecosystem integration
  • Vendor strategy aligned to decision rights

  Representative: Salesforce ecosystem, Contentful, global martech stacks

• Product organization, leadership, and investment

  Builds portfolio governance, KPI frameworks, and PM operating models so teams ship against one investment narrative.

  • Budget ownership and prioritization forums
  • Cross-functional alignment without consensus theater
  • PM development and delivery accountability

  Representative: Portfolio forums, KPI frameworks, operating model design

Hands-on fluency across data, AI, cloud, and delivery systems I use to spec and ship with engineering partners.

• Data and analytics

  Builds and specs pipelines, semantic layers, and reporting products that turn fragmented sources into decisions product and finance can act on.

  • SQL and warehouse-centric analytics design
  • Looker and Tableau product surfaces
  • ETL and orchestration with data engineering

  Representative: Snowflake, Databricks, Python, SQL, Looker, Tableau, Airflow

• AI/ML

  Ships generative and ML-backed products with clear data boundaries, evaluation criteria, and integration paths into existing workflows.

  • AI product strategy and ML integration
  • Privacy-safe data architecture
  • Generative AI product patterns

  Representative: AI product strategy, ML integration, generative AI products

• Cloud and application stack

  Partners with engineering on cloud-native delivery, APIs, and front-end surfaces when the product requires direct technical depth.

  • AWS, GCP, and Azure deployment patterns
  • API and service integration design
  • Prototyping with modern web stacks

  Representative: AWS, Google Cloud, Azure, Python, JavaScript, React, Node

• Product delivery and enterprise systems

  Runs delivery across Agile frameworks and enterprise systems so roadmaps, backlogs, and client platforms stay aligned to outcomes.

  • Jira, Confluence, ProductBoard, Figma
  • CRM, PIM, and commerce platform integrations
  • Agile, Scrum, and SAFe at scale

  Representative: Jira, ProductBoard, Figma, Salesforce, Contentful, SAFe

How I design, govern, and run agentic workflows as an operating layer for product delivery, not a pile of disconnected tools.

• Governed operator systems

  Assigns one Active Writer per task across registry, Agent Orchestration Layer, audit layer, and registered integrations at operator scale.

  • Operator Control Plane design and operation
  • Registry and task ownership discipline
  • Registered integrations without duplicate writes

  Representative: Agent Orchestration Layer, Knowledge Base, governed automation registry

• Automation fleet and certification

  Runs 39 scheduled automations with explicit ownership so overnight work produces morning signal without conflicting writes.

  • Scheduled execution at governed scale
  • Certification rhythm for skills and automations
  • Audit cycles that keep the system current

  Representative: Agent Orchestration Layer, scheduled task registry

• AI-native prototyping

  Prototypes products and workflows with LLM-native tools while keeping production boundaries and governance explicit.

  • Rapid prototyping with execution and audit LLMs
  • Workflow automation with agents and orchestration
  • HTML/CSS surfaces for proof artifacts

  Representative: Multi-LLM orchestration, agent workflows, proof surfaces

• Decision systems and proof

  Builds decision systems with one pipeline, explicit ownership, and proof artifacts that show how product and platform choices hold up under scrutiny.

  • Decision rights for product and platform work
  • Proof portfolios tied to operating model
  • Cross-session memory and continuity discipline

  Representative: Decision rights, proof-at-a-glance systems