Sr Data Engineer

The Walt Disney Company•Orlando, FL

About The Position

Enterprise Data Platform & Product Enablement Role Summary: The Senior Data Engineer will play a critical role by designing and delivering scalable enterprise data solutions that support multiple Products. This role is responsible for establishing and maintaining the enterprise data platform, building trusted datasets, and enabling cross-product analytics, reporting, and AI-driven insights. Working across products and technology disciplines, this role will architect and engineer robust data pipelines, data models, and integration patterns that ensure consistent, high-quality data is available to support decision-making, executive reporting, and advanced analytics.

Requirements

A minimum of 5 years of experience in data engineering, data architecture, or enterprise data platform development.
Proven experience designing and supporting enterprise data pipelines and data warehouse / lakehouse solutions.
Strong expertise in SQL and Python.
Experience with cloud data platforms (e.g., Snowflake, AWS, Azure) and hybrid data integration patterns.
Hands-on experience with ETL / ELT orchestration tools and data pipeline automation.
Strong understanding of data modeling, semantic layer design, and performance optimization techniques.
Experience supporting BI and analytics platforms such as Power BI, Tableau, or similar tools.
Familiarity with data governance, metadata management, and data quality frameworks.
Ability to collaborate effectively across product teams, engineering disciplines, and business stakeholders.
Strong analytical thinking, problem-solving capability, and communication skills.
Experience designing and engineering data solutions that support artificial intelligence, machine learning, or advanced analytics use cases.
Familiarity with AI-enabled capabilities within modern data platforms and visualization tools (e.g., Snowflake Cortex, Power BI Copilot, Tableau AI, or similar technologies).
Understanding of data preparation techniques for AI including feature engineering, data enrichment, and dataset optimization.

Nice To Haves

Experience supporting enterprise data product models or platform-based operating structures.
Exposure to machine learning data preparation, AI data pipelines, or advanced analytics environments.
Experience implementing data observability or data reliability engineering practices.
Background working in Agile delivery models with cross-functional product teams.
Hands-on experience enabling AI or machine learning workflows within enterprise data environments, including support for model data pipelines, intelligent data products, or automated insight generation.

Responsibilities

Enterprise Data Platform Engineering Design, build, and optimize scalable data pipelines and integration frameworks within the existing DXT ecosystem in accordance with DXT data standards, to support multiple B2B data products /sources systems and enterprise reporting needs.
Architect and implement data ingestion, transformation, and storage patterns across cloud and hybrid data environments.
Establish reusable data engineering standards and best practices to enable consistency and scalability across product domains.
Develop curated enterprise datasets that serve as trusted sources for dashboards, analytics, and AI initiatives.
Reporting & Analytics Data Foundations Design and maintain data layers that support executive dashboards, operational KPIs, and enterprise reporting.
Ensure data quality, lineage, and performance standards are met for datasets consumed by BI platforms, AI tools and downstream analytical solutions.
Collaborate with analytics teams to optimize data structures for AI enablement, visualization, self-service analytics, and advanced modeling.
AI-Enabled Data Platform & Advanced Analytics Support Design and implement data pipelines and data models that enable artificial intelligence and machine learning use cases, including predictive analytics, anomaly detection, and intelligent automation.
Prepare and curate high-quality feature datasets to support AI model development, training, and deployment across enterprise data products.
Enable integration of AI capabilities within cloud data platforms and BI tools to accelerate insights and improve decision support.
Establish scalable data engineering patterns that support real-time or near-real-time data consumption for AI-driven workflows.
Data Governance, Quality, and Reliability Implement data validation, monitoring, and observability processes to ensure reliable and trusted data delivery.
Maintain documentation, metadata standards, and data definitions supporting enterprise governance and compliance requirements.
Proactively identify opportunities to improve pipeline performance, data usability, and architectural efficiency.
Platform Evolution & Innovation Support modernization initiatives including cloud data platform expansion, automation, and AI readiness.
Evaluate and implement new technologies and approaches that enhance data scalability, resilience, and time-to insight.
Contribute to the evolution of the organization’s enterprise data strategy and operating model maturity.