Senior Manager, Data Science & Analytics

Sesame Workshop•New York, NY

1d•Hybrid

About The Position

The Senior Manager, Data Science & Analytics is a member of Research & Insights and reports to the Senior Director, Data Science. This is the first dedicated hire within the Data Science function and acts as the product owner for Sesame Workshop's shared data models and analytics layer. While the knowledge of what each source contains lives with its data owners across the business, this role knows that landscape end-to-end, documents it, and turns it into maintainable, well-modeled data products in dbt that the organization's data analysts and business-intelligence (BI) partners build on. This role owns the models and their trustworthiness, while those analysts own how the data is presented to stakeholders. Sesame Workshop is building its internal data capabilities to better serve teams across the organization: from Marketing and Strategy to Revenue and Impact Programs. The Senior Manager, Data Science & Analytics exists to own data models, transformation logic, and metric standards that turn raw, scattered data into a dependable analytics layer. This is a product-ownership role for the analytics layer. The right candidate is quick to learn and adapt to the ever-changing data landscape, documents how each measure is calculated and to what quality standard, and encodes it as versioned, maintainable data products in dbt. This provides the backbone that lets analysts turn well-modeled data into dashboards, reports, and presentations. This is a hands-on, senior individual-contributor role with room to grow into broader technical leadership and data governance. The Senior Manager operates independently, is well organized, sets standards rather than waiting for direction, and finishes things: taking prototypes or proofs of concept and turning them into data products that run reliably. As Sesame Workshop's data practice matures, the role is positioned to own data governance operations for the organization.

Requirements

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).

Nice To Haves

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.

Responsibilities

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.
Build and maintain shared datasets that combine and standardize data from across the organization's source systems, so analysts can work from consistent, reliable data and build their own reports rather than manually pulling and reconciling exports.
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.
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.
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.
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.
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.