The Disney Streaming Subscriber Analytics organization is focused on measuring and understanding the growth of the entire Disney Streaming business (Disney+, ESPN+, and Hulu). This includes the entire subscriber journey, from acquisition to churn. Under the Subscriber Analytics organization, the Analytical Engineering team manages critical subscriber datasets from which the entire business measures subscriber growth and churn.
We are seeking a Senior Analyst who will act as a subject matter expert for all facets of Subscriber Churn, a critical and complex focus area of the business. This ranges from understanding the lineage of our data pipelines to mastering metric definitions. All of which is critical for designing and implementing core Subscriber Churn data models that enable internal clients to explore, analyze, and monitor trends. Additionally, given the foundational nature of this dataset, this Senior Data Analyst will collaborate widely across the entire organization, working with Product, Data Engineering, Data Science, Finance, and Business Ops to understand their data requirements and to make sure our data models fully meet their needs and are future-proofed for new initiatives.
Build and maintain data models that enable reporting and analysis of subscriber churn behavior
Develop metrics and analytical frameworks that measure and support overall understanding of business decision success relative to subscriber growth
Partner with analytics and business stakeholders to understand churn data requirements, design scalable solutions, and socialize new data assets and usage best practices
Troubleshoot and resolve data pipeline issues, perform root cause analysis and implement recovery and backfill processes to ensure smooth data flow
Develop documentation and visualizations that provide analytical support for new metrics & concepts that both technical and non-technical audiences can understand
Champion data quality by fostering relationships with engineering partners and minimize technical debt where possible
Bachelor’s degree in business, economics, mathematics, statistics, computer science, or related field
5+ years work experience in an analytical, technical or business operations role
Proficiency in SQL and experience with other programming languages like Python
Experience in processing, transforming and analyzing large datasets in data warehousing platforms like Snowflake, Redshift, etc.
Experience in developing data models and pipelines using workflow orchestration tools like Airflow, Databricks, etc.
Experience with Git and version control platforms like Github, GitLab, etc.
Experience with data exploration and data visualization tools like Tableau, Looker, etc.
Strong communication skills, as well as written and verbal presentation skills
Proven track record to solve ambiguous problems independently
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