Staff Data Engineer

Choice Hotels International, Inc.; Scottsdale, Phoenix (AZ, AZ); On-site; USD 120,000 - 160,000; Full time

Job summary

Choice Hotels International, Inc. is seeking a Staff Data Engineer to work in its SkyTouch Technology division. This role involves defining and evolving long-term data architecture and driving data initiatives across teams.

The ideal candidate will have over 8 years of experience in data engineering and proven expertise in AWS. This hybrid position offers the opportunity to mentor talented engineers while ensuring high-quality, scalable data solutions.

Qualifications

• Minimum of 8+ years of experience in data engineering, software engineering, or platform engineering roles.
• Proven experience designing and evolving large-scale, cloud-based data platforms in AWS.
• Experience leading cross-team, multi-system data initiatives with long-term architectural impact.

Responsibilities

• Define and evolve the long-term data architecture and technical standards across teams.
• Design and oversee highly scalable, resilient, and cost-efficient data systems using cloud technologies.
• Mentor senior engineers and influence technical growth through design reviews.

Skills

Cloud data architectures

Python

SQL

Spark

Data modeling

Data warehousing

AWS data services

CI/CD

Automation

Distributed systems

Education

Bachelor’s degree in Computer Science, Information Systems, Computer Engineering, or a related field

Job description

This role is not eligible for sponsorship AND is four days onsite hybrid at our N. Scottsdale office

Choice Hotels is looking for a Staff Data Engineer in its SkyTouch Technology division, responsible for driving enterprise‑wide data architectural maturity and influencing cross‑team initiatives.

Responsibilities

• Define and evolve the long‑term data architecture and technical standards across teams and platforms.
• Design and oversee highly scalable, resilient, and cost‑efficient data systems using cloud technologies such as Amazon Web Services.
• Make principled trade‑offs between AWS‑managed services and open technologies (e.g., Spark, Flink, Iceberg) to ensure long‑term scalability and maintainability.
• Lead complex, cross‑domain data initiatives that span multiple teams, systems, and business functions.
• Serve as a technical authority and escalation point for complex data engineering challenges and architectural decisions.
• Establish best practices for data quality, reliability, observability, security, and governance across the organization.
• Drive alignment on data modeling, batch and real‑time integration patterns, and platform usage to reduce duplication and technical debt.
• Partner with engineering, analytics, and business leaders to translate strategic goals into technical data solutions.
• Mentor senior engineers and influence technical growth through design reviews, architectural forums, and technical guidance.
• Use an AI‑first approach to mitigate systemic risks, improve engineering productivity focusing on automation and operational efficiency.
• Balance near‑term delivery with long‑term platform health, scalability, and sustainability.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Computer Engineering, or a related field.
• Minimum of 8+ years of experience in data engineering, software engineering, or platform engineering roles.
• Proven experience designing and evolving large‑scale, cloud‑based data platforms in AWS.
• Experience leading cross‑team, multi‑system data initiatives with long‑term architectural impact.
• Demonstrated ownership of mission‑critical production data systems.
• Experience influencing technical direction across teams without direct people management.
• Expert‑level knowledge of cloud data architectures and AWS data services.
• Advanced data modeling and data warehousing design expertise.
• Strong programming skills in Python, SQL, and/or Spark.
• Deep understanding of distributed systems, performance tuning, and scalability.
• Infrastructure‑as‑code, CI/CD, and automation expertise.
• Ability to design scalable, fault‑tolerant, and cost‑efficient data architectures.
• Strong knowledge of data quality, observability, and reliability practices.
• Knowledge of data governance, security, and compliance best practices.
• Strong debugging and root‑cause analysis skills across data pipelines and cloud infrastructure.
• Experience designing for reliability, observability, fault tolerance, and cost efficiency at scale.
• Experience implementing organization‑wide standards for data quality, security, and governance.
• Experience identifying and reducing technical debt across data platforms.
• Experience applying AI/GenAI in production to engineering workflows, automation, data quality, or developer productivity.

Team & Reporting

• Individual‑contributor role reporting to the Manager of Software Engineering – Data.
• Collaborates with 14 peer teammates and cross‑functional departments on a regular basis.

This role is not eligible for sponsorship