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About This Role
“We Power the Magic!” That’s our motto at Disney Experiences (DX). Our team creates world\-class immersive digital experiences for the Company’s premier vacation brands including Disney’s Parks \& Resorts worldwide, Disney Cruise Line, Aulani, a Disney Resort \& Spa, and Disney Vacation Club.
We are responsible for the end\-to\-end digital and physical Guest experience for all technology \& digital\-led initiatives across the Attractions \& Entertainment, Food \& Beverage, Resorts \& Transportation and Merchandise lines of business as well as other initiatives including MyDisneyExperience and Hey, Disney!
This role sits in the (DAI) “Data Analytics \& Innovation” organization within (DXi) “Disney Experiences Intelligence.
The Data Engineer III role, will report to the Senior Manager, Data Services.
About The Role \& Team:
The Data Analytics \& Innovation (DAi) team within Disney Experiences Intelligence (DXi) builds and operates the data platforms, AI\-powered applications, and intelligence systems that turn raw data into insight and action across Disney's premier vacation brands. We are actively modernizing onto a cloud\-native data platform while building AI applications from the ground up on our internal AI frameworks.
We are looking for an engineer first. Someone who can sit with an ambiguous, complex problem, listen carefully to what the business actually needs, and design an optimized solution rather than reach for the first pattern that fits. This role goes well beyond ETL. As a Data Engineer III, you will engineer AI\-powered applications end to end, from architecture and design through build and delivery, using our AI frameworks as the foundation.
You will bring core data disciplines (pipelining, transformation, and the full data lifecycle) and apply them with strong engineering judgment: weighing tradeoffs, designing for performance and scale, and building solutions that hold up over time. You will also work directly with our partners in Data Strategy and AI Innovation, fellow teams under the DAi umbrella, to align on priorities and turn strategy into working solutions. Just as important, you will thrive in an AI\-powered environment that changes daily. The tools, frameworks, and capabilities available to us evolve constantly, and we need engineers who stay curious, adapt quickly, and figure out how to put new capabilities to work rather than wait for the ground to settle.
What You'll Do:
- Contribute to the design, construction, and supervision of technology architecture, solutions, and software dedicated to capturing, managing, and using both structured and unstructured data from various sources
- Develop efficient processes and structures based to streamline data flow, routing, and storage, adhering to both business and technical specifications, and leveraging cloud and local storage options as required
- Apply technical tools and programming skills for the purposes of data cleansing, organization, transformation, and upkeep, integrating automation and advanced methodologies such as big\-data, artificial intelligence, and machine learning
- Uphold design standards and quality assurance protocols for the development of software and systems, ensuring seamless data compatibility, functionality, and integrity across all data connections, transmissions, and storage platforms
- Evaluate internal and external business and product requirements to ensure alignment of data operations and endeavors with overarching organizational objectives and priorities
- You will be expected to stay up to date with emerging technologies
- May be required to provide support escalation, oversee the ongoing operations, and support the enhancement of existing tools as needed
Required Qualifications:
- Minimum 5 years of related work experience
- Proficiency in fundamentals of data pipelining, ELT/ETL processes, data architecture, and the overall data lifecycle, complemented by hands\-on experience in designing and implementing efficient data workflows and processes
- Strong development skills in cross\-platform languages such as SQL, Python, Java, and Apache Spark coupled with a deep understanding of SQL/TSQL for crafting performant queries and extracting insights from complex datasets
- Experience with both relational database and NoSQL databases (e.g., Cloudera Data Platform, Snowflake, Google Big Query, MongoDB, DynamoDB, Redis, HBase, Cassandra), demonstrating competence in working with diverse storage systems and optimizing data structures for enhanced performance and scalability
- Familiarity with cloud technologies including AWS and Google Cloud Platform (GCP), demonstrating their powerful toolsets for seamless data ingestion, processing, and management
- Comfortable working in an agile environment, with prior experience in Agile and Scrum methodologies, ensuring adaptability to evolving project requirements and effective collaboration with cross\-functional teams
- Proficiency in Object Oriented Programming and Big Data Engineering Concepts, with a proactive demeanor towards continuous learning and skill development, and an interest in mastering emerging data engineering tools and methodologies
Preferred Qualifications:
- AI \& Application Development \- Hands\-on experience building AI\-powered applications, ideally on an internal or managed AI framework (agentic workflows, LLM integration, retrieval\-augmented generation, and orchestration/tooling patterns such as MCP)
- Languages \& Engineering \- Strong Python proficiency with sound software engineering practices (modularity, testing, code review); SQL/TSQL for performant query design
- Modern Data Platform \- Experience with lakehouse architectures (open table formats such as Apache Iceberg on object storage) and cloud data warehousing/compute (Snowflake)
- Data Processing \- Spark, Glue, Flink, or equivalent distributed processing frameworks; orchestration tooling (e.g., Airflow)
- Cloud (AWS preferred) \- Core AWS data and compute services such as S3, Lambda, Glue, Kinesis, DMS, and EventBridge
- CI/CD \& DevOps \- Pipeline automation with GitLab, GitHub, or Jenkins; containerization with Docker and orchestration (ECS/EKS)
- Security \- IAM roles, KMS, wire encryption, and enterprise auth (OAuth2/SAML, Active Directory)
Required Education:
- Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience
\#DISNEYTECH
The hiring range for this position in Orlando, FL is $135,200 \- $181,200 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job\-related knowledge, skills, and experience among other factors. A bonus and/or long\-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
Salary Context
This $135K-$181K range is above the median for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Disney Experiences, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($158K) sits 14% below the category median. Disclosed range: $135K to $181K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Disney Experiences AI Hiring
Disney Experiences has 1 open AI role right now. They're hiring across Data Engineer. Based in Celebration, FL, US. Compensation range: $181K - $181K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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