Disney Streaming is seeking a Lead Analytical Engineer to join the Product Performance and Forensics Analytics team within the broader Product & Technology Analytics function. In this highly visible and technically complex role, you’ll be responsible for developing and maintaining scalable data pipelines, creating core business logic, and ensuring high data quality standards across browse telemetry and product health metrics.
You’ll collaborate closely with analysts, data engineers, product managers, and engineering partners to turn raw telemetry into trusted datasets, dashboards, and insights that power decision-making across Disney+, Hulu, and ESPN. This role is ideal for someone who thrives at the intersection of engineering and analytics and wants to drive data excellence at scale.

REQUIRED QUALIFICATIONS

• Bachelor’s degree in Economics, Statistics, Analytics, Computer Science, MIS, Data Engineering, or related field
• 7+ years of experience in analytical engineering, data engineering, or data analytics with strong SQL and data modeling expertise.
• Deep understanding of ETL/ELT principles, data architecture, and performance optimization.
• Proven experience building scalable data products and pipelines using tools such as Databricks, Airflow, Spark, or similar platforms.
• Strong ability to collaborate with both technical and non-technical stakeholders to deliver high-impact data solutions.
• Fluency in building and maintaining production-grade datasets in cloud-based data environments (e.g., Snowflake, Redshift, BigQuery).
• Proficiency with BI tools such as Looker and Tableau, including building curated data layers and performance tuning.
• Strong data hygiene practices, including version control, code reviews, unit testing, and documentation.
• Excellent problem-solving skills and attention to detail.

PREFERRED QUALIFICATIONS

• Experience working with browse telemetry or event-based product instrumentation data.
• Proficiency in Python or another scripting language for data transformation or orchestration.
• Familiarity with streaming media or direct-to-consumer digital products.
• Experience implementing anomaly detection systems or contributing to root cause investigations.
• Knowledge of data governance, privacy, and compliance best practices.

• Pipeline Ownership: Design, build, and maintain robust, scalable, and efficient data pipelines in collaboration with data engineering and analytics teams.
• Data Modeling: Develop and refine trusted, reusable data models and core logic that power key metrics and dashboards across the organization.
• Data Quality: Partner with software and data engineering teams to ensure data completeness, accuracy, and consistency, particularly for browse telemetry and product instrumentation.
• Performance Monitoring: Contribute to anomaly detection systems and operational dashboards that track product health, user flows, and data quality KPIs.
• Cross-functional Collaboration: Work closely with analysts, product managers, and engineers to translate business needs into technical data solutions.
• Tooling & Enablement: Build scalable, performant datasets in platforms such as Databricks, Looker, and Snowflake to enable self-service analytics.
• Mentorship & Leadership: Provide technical guidance and best practices to junior analytical engineers and analysts on topics such as pipeline optimization, testing, and documentation.
• Documentation & Standards: Champion the creation and maintenance of clear technical documentation and establish coding standards and data governance practices.