Senior Manager, Data Science & Analytics

About the role

Sesame Workshop is the global nonprofit behind Sesame Street and so much more. For over 50 years, we have worked at the intersection of education, media, and research, creating joyful experiences that enrich minds and expand hearts, all in service of empowering each generation to build a better world. Our beloved characters, iconic shows, outreach in communities, and more bring playful early learning to families in more than 190 countries and advance our mission to help children everywhere grow smarter, stronger, and kinder.

Responsibilities & Delivery

• Own the data models and documentation that encode the organization's key metrics, capturing, from each source system's data owners, which source answers which question, how each measure (e.g., "reach," "engagement," "revenue") is calculated, and the quality bar it must meet.
• Build and maintain the data models and transformation logic (e.g., dbt) that implement those definitions, turning proof-of-concept analyses and prototype tools into documented, tested, version-controlled data products that downstream users can depend on without ongoing intervention.
• Manage data quality and documentation as a product, maintaining a catalog of the active data models and metrics (their sources, refresh schedules, and known limitations), keeping metric definitions and methodology transparent and easy to inspect (such as dbt docs) so the numbers are understood and trusted across teams, and proactively flagging data source changes or data-quality issues before they reach downstream users.
• Integrate and model data from multiple systems, including but not limited to local databases, cloud object storage, Google Analytics, and Salesforce, into unified, reusable datasets that feed leadership-level and board reporting.
• Improve the performance and reliability of the analytics layer by automating manual workflows, optimizing queries and downstream extracts and data preparation (e.g., for BI tools), and recommending improvements to data-collection practices.

Collaboration & Teamwork

• Partner with the Senior Director, Data Science, to set the roadmap for shared data products, prioritize what gets modeled, and align the analytics layer with the function's strategic initiatives.
• Coordinate with the Technology team on data infrastructure, access, and governance, and leverage shared platforms such as Snowflake.
• Partner with data analysts and BI colleagues across departments (e.g., Consumer Insights, Marketing) as the internal customers of the analytics layer, so that data products serve the questions teams need to answer.

Stakeholder & Relationship Management

• Build trusted working relationships with these teams by delivering reliable, well-documented data products they can build on with confidence.
• Cultivate an understanding of how each team uses data so that models, metrics, and documentation match the questions they need to answer.
• Foster a reputation as the person who turns shared metrics into consistent, well-documented models the whole organization can rely on, rather than the person who fields ad-hoc requests for them.

Communication & Influence

• Advocate for consistent metric definitions and data-quality standards across departments, and document them so they are discoverable and reusable.
• Consult with analysts and business owners on how to translate questions into well-defined, trackable metrics, establishing the definition and quality bar.

Cross-functional & Strategic Engagement

• Align the shared metric layer with official organizational priorities and the Strategy team's KPI framework, so the numbers reported across departments are consistent and reinforce the metrics leadership has endorsed.
• Break down data silos by partnering across departments to bring together data that lives in separate systems, so the organization can answer cross-cutting questions, like total audience reach across platforms, that no single team can answer alone.

Required Qualifications

• 5+ years of professional experience in data modeling, data transformation, or building and owning shared data assets that other analysts and teams rely on.
• Strong SQL proficiency, including writing and optimizing complex queries across large, multi-source datasets.
• Python proficiency for data transformation, automation, and internal tooling, with comfort using modern developer workflows, including version control (git).
• Hands-on experience with a data transformation framework (dbt preferred).
• Demonstrated ownership of data quality and metric definitions: defining canonical metrics, setting quality standards, and maintaining documentation or a data dictionary as a product.
• A product-owner mindset for data: knowing the full data landscape, documenting it, and turning it into maintainable, well-modeled data products, while prioritizing what to build and setting modeling and quality standards proactively rather than building one-off queries on request.
• Ability to enable and upskill analysts through clear documentation, pairing, and code review.
• Bachelor's degree in a quantitative field (statistics, economics, data science, computer science, mathematics, social science with quantitative methods, or similar).

Preferred Qualifications

• Experience working in a nonprofit, media, or mission-driven organization where data maturity is still developing and processes must be built from scratch.
• Familiarity with a cloud data warehouse (e.g., Snowflake), Google Analytics, or Salesforce data.
• Familiarity with a BI tool (Tableau, Looker, or Power BI), particularly optimizing extracts and data preparation for performance.
• Experience establishing data governance, a semantic layer, or a metrics catalog.
• Comfort working in the terminal/command line and with AI coding assistants/agentic tooling used to accelerate development.
• Comfort operating in an ambiguous environment with evolving priorities and limited formal process, the kind of person who creates structure rather than waiting for it.
• Exposure to audience analytics or media measurement (viewership metrics, digital engagement, attribution modeling).
• Interest in the intersection of data, education, and social impact.