Technical Program Manager, Data & Reporting

Snowflake•Menlo Park, CA

About The Position

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. The Mission Our Global Revenue Enablement organization operates as a unified enablement engine with a clear mission: to equip every audience in our ecosystem with the clarity, content, and skills they need to execute with speed and consistency. As part of the Program Management & Reporting (PM+R) team, you will architect the metrics & signals strategy. Your work will ensure that enablement drives measurable adoption, shortens time-to-value, and directly impacts GTM outcomes. The Role We are seeking a Senior Reporting Analyst who possesses a "jack-of-all-trades" data & AI mentality and an understanding of how people learn and adopt new behaviors. This is not a traditional reporting role where you simply export completion reports from an LMS. This is a strategic builder role for an experienced individual contributor who can operate autonomously. You will own the roadmap for our Business Intelligence (BI) tools, identifying early signals of risk and success. You will partner in the shift in our measurement maturity model from Engagement (Did they take it?) to Proficiency (Did they learn it?) to Adoption (Are they using it?) and finally to Impact (Did it change business outcomes?).

Requirements

Experience: 5-8+ years in Business Intelligence or Data Analytics. Requires a track record of autonomous execution.
Technical Stack: Advanced proficiency in SQL, experience with Python & AI tools. Experience with Snowflake, Sigma, transformation tools and Cursor is highly preferred.
Analytical Mindset: Demonstrated ability to translate abstract business questions into concrete data models and executable queries.
Communication: You don't just show the chart; you explain the business implication that drives a recommended next step.

Nice To Haves

Enablement/Education Background: Experience working with Sales Enablement, Learning & Development, or EdTech. You understand the difference between a "knowledge gap" and a "skill gap."
SaaS Consumption Models: Experience analyzing data in a consumption-based business model (analyzing usage trends, burn rates, and adoption curves).
AI/LLM: Experience or strong interest in how Generative AI can be applied to learning analytics or productivity measurement.
Data Migration: Experience migrating data between systems.

Responsibilities

Metrics & Signals: Define outcome-based metrics & KPIs moving beyond vanity metrics to leading indicators of user adoption and usage.
Stakeholder Alignment: Collaborate with enablement stakeholders (Field, Customer, Partner) to establish baseline proficiency metrics & business requirements.
System Ownership: Collaborate with Snowflake’s Data Analytics team to build and maintain the Global Revenue Enablement reporting suite using Sigma, Snowflake & Streamlit.
Real-Time Visibility: Collaborate with Snowflake’s Data Analytics team to develop dashboards that provide real-time visibility into the readiness of our GTM teams.
Proactive Analysis: Perform ad-hoc deep dives to validate hypotheses (e.g., "Does certification X correlate with higher expansion revenue in the following rollout quarter?").
Narrative Construction: Translate complex datasets into clear, actionable narratives. You will tell the story of where enablement is working and where we need to pivot in our learning experience.
Friction Analysis: Identify "Time to Value" friction points—analyzing where learners stall in their journey and recommending interventions to leadership.
Future-Proofing: Partner with the Enablement Tech & Data team to ensure our data infrastructure is "AI-Ready."
AI Analytics: Experiment with AI-driven analytics to identify patterns to measure qualitative shifts in field behavior.
Navigating an Evolving Data Stack: Thrive in a dynamic environment where data models shift, while contributing & collaborating closely with infrastructure solution architects to build reporting across maturing schemas.