Senior Data Engineer

The Walt Disney Company

Senior Data Engineer building Disney’s acquisition marketing data pipelines across Databricks, AWS, Snowflake, and Airflow. Powering reliable reporting and analytics for Disney media businesses.

Posted 8/18/2026full-timeSanta Monica • California, New York, Washington • 🇺🇸 United StatesSenior💰 $141,900 - $199,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and maintaining scalable ETL/ELT pipelines using Databricks, PySpark, and SQL, with a strong focus on data governance and quality assurance. Proven ability to collaborate with cross-functional teams to deliver reliable data solutions and mentor junior engineers.

Highest-signal resume keywords

ETL/ELT Pipeline DevelopmentDatabricks and Unity CatalogSQL ProficiencyData Quality FrameworksAWS Services

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPySparkDatabricksETL/ELT Design PatternsData ModelingAirflowJSONParquetCI/CDDevOps Practices

Soft Skills

Excellent CommunicationCross-Functional CollaborationMentoring

Tools & Technologies

AWS S3AWS IAMAWS EC2AWS GlueAWS LambdaGitGitHub

Industry Keywords

Data EngineeringData GovernanceData ObservabilityIncident WorkflowsMarketing Data

Tech Stack

Tools & technologies

AirflowAWSEC2ETLPySparkSparkSQLUnity

About the role

Key responsibilities & impact

Architect, build, and maintain scalable ETL/ELT pipelines for acquisition reporting using Databricks, PySpark, SQL, and Unity Catalog
Lead modernization of Snowflake-based SQL scripts and transformations into Databricks Unity Catalog using governance and structured-access best practices
Design robust ingestion frameworks for marketing vendor data
Implement data quality checks, monitoring, and automated remediation using Databricks, Snowflake, Airflow, and internal frameworks
Develop metadata-driven, parameterized pipeline components to accelerate onboarding of new vendors and datasets
Partner with Data Reliability Engineering to integrate SLA-based incident detection, logging, alerting, and auto-recovery workflows
Collaborate with analytics and marketing stakeholders to understand reporting needs and ensure reliable dashboard data
Improve pipeline performance, reliability, logging, and observability
Contribute to engineering best practices, code reviews, technical design documents, and framework enhancements
Mentor junior engineers and contribute to team-wide architectural decisions

Requirements

What you’ll need

5+ years of experience as a Data Engineer or similar role
Strong proficiency in SQL, including analytical SQL, complex joins, and window functions
Hands-on production experience with PySpark and/or Spark SQL
Strong understanding of data modeling, ETL/ELT design patterns, and distributed data processing
Experience building pipelines in Databricks, including Delta Lake, Unity Catalog, data governance, and Lakehouse patterns
Strong experience with AWS, including S3, IAM, EC2, Glue, Lambda, or related services
Proficiency with Airflow or similar orchestration tools
Experience building robust ingestion pipelines and working with JSON, Parquet, and CSV
Experience with Git/GitHub, CI/CD, and modern DevOps practices
Excellent communication skills and ability to work with cross-functional partners
Bachelor’s degree in computer science, Information Systems, or related field
Master’s degree in computer science, Information Systems, or related field is a plus
Experience with marketing or customer acquisition data is preferred
Familiarity with data observability, SLA monitoring, incident workflows, or reliability engineering concepts
Exposure to data quality frameworks such as Great Expectations, Deequ, Monte Carlo, or custom frameworks

Benefits

Comp & perks

Bonus and/or long-term incentive units may be provided
Medical, financial, and/or other benefits
Primarily on-site / occasionally from home