Data Product Mgr
Job Summary:
As a Data Product Manager at Disney Experiences, you will play an important role in supporting the lifecycle of our data products. You will begin building your expertise at the intersection of business partnership and data — learning to translate stakeholder needs into clear data product requirements and working alongside Data Product Engineers to help bring those requirements to life.
This role is designed for a curious, detail-oriented individual who is eager to grow their product management skills within a modern, cloud-native data environment. You will develop familiarity with platforms like Snowflake and dbt, contribute to requirements, support user acceptance testing, and help drive adoption of data products across internal business teams. This role is for someone who wants to grow into a data product leader and is excited to learn what it means to treat data as a product.
What You'll Do:
Business Partnership & Discovery: Support requirements gathering by engaging with business stakeholders to understand their data needs, pain points, and priorities. Assist in documenting stakeholder interviews, capturing use cases, and identifying opportunities where data products can deliver value. Contribute to maintaining an organized backlog of data product requests and enhancement ideas.
Data Product Requirements: Assist in translating business needs into data product requirements, user stories, and acceptance criteria. Work with senior product managers and Data Product Engineers to refine requirements and ensure they are clear, complete, and actionable. Help maintain data product documentation including use case descriptions, field definitions, and expected outputs.
Cross-Functional Collaboration: Work alongside Data Product Engineers, Data Architects, and senior product managers to support the delivery of data products. Participate in sprint ceremonies, backlog refinement sessions, and cross-functional syncs to stay aligned on progress and priorities. Develop your understanding of the technical stack — including Snowflake schemas, dbt transformations, and AWS data services — through regular collaboration with engineering teams.
User Acceptance & Adoption: Participate in User Acceptance Testing (UAT) by coordinating sessions, tracking feedback, and documenting outcomes. Support the communication and rollout of new and updated data products, helping business users understand what has been built, how to access it, and how to use it effectively.
AI/ML Data Support: Assist in identifying data readiness requirements for AI/ML and data science initiatives. Help gather and document requirements related to feature engineering, training data preparation, and data quality thresholds needed to support machine learning workflows.
Quality & Governance: Support data governance practices by helping to document data lineage, metadata, and quality standards for data products. Assist in monitoring data product quality and flagging issues to the appropriate engineering or governance teams for resolution.
Required Experience & Skills:
Minimum of 3+ years of experience in product management, business analysis, data analysis, or a related role, with an interest in data products, business intelligence, or data-driven decision-making.
Technical Familiarity: Basic understanding of SQL for querying and reviewing data — comfort reading and writing simple queries is a plus. Awareness of cloud data concepts and platforms such as Snowflake, AWS, or GCP — formal experience is not required but curiosity and a willingness to learn are essential. Familiarity with dbt, data pipelines, or ETL/ELT concepts is a plus but not required. Exposure to AI/ML workflows or data science concepts is an advantage.
Product Foundations: Working knowledge of product management principles and Agile/Scrum methodologies. Experience writing user stories, contributing to a product backlog, or participating in sprint planning is a plus.
Data Literacy: A foundational understanding of how data is structured, stored, and used — including basic familiarity with concepts like data models, data quality, and analytics. An eagerness to deepen this knowledge over time.
Communication: Clear written and verbal communication skills, with the ability to summarize information for both technical and non-technical audiences. Comfortable asking questions, documenting decisions, and following up on open items.
Required Education:
Bachelor's degree in Business, Computer Science, Information Systems, Data Science, or a related field, and/or equivalent work experience.
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