Staff Data Engineer
About The Position
This role is not eligible for sponsorship AND is four days onsite hybrid at our N. Scottsdale office. Who are we looking for? Choice Hotels has an exciting new opportunity as a Staff Data Engineer in the SkyTouch Technology division. SkyTouch Technology, is an independently operated division of Choice Hotels that provides the most widely used cloud-based (SaaS) hotel property management system. As a key member of our SkyTouch Software Engineering - Data, you will drive enterprise-wide impact by shaping scalable, reliable data architectures and standards that enable teams to deliver trusted data, accelerate innovation, and support long-term business strategy. Are you a systems-level data engineer who thinks beyond individual pipelines to shape enterprise-wide data architecture and standards? Do you thrive on influencing technical direction and enabling multiple teams to succeed at scale? The #SkysTheLimit when you #MakeItYourChoice! We encourage you to apply today!
Requirements
- Bachelor's degree in related field Computer Science, Information Systems, Computer Engineering or equivalent experience
- 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.
- Acts as a trusted technical authority, guiding architectural decisions, mentoring senior engineers, and influencing outcomes across teams.
- Strong programming skills (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 understanding of distributed systems, data partitioning, and performance tuning
- Expertise in 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.
- Designing for reliability, observability, fault tolerance, and cost efficiency at scale
- Implementing organization-wide standards for data quality, security, and governance
- Identifying and reducing technical debt across data platforms
- Experience applying AI/GenAI in production to engineering workflows, automation, data quality, or developer productivity
- Demonstrates key competencies to include managing complexity, valuing differences, and cultivating innovation
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 (e.g., AWS)
- Make principled tradeoffs 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, architecture 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
Number of Employees
501-1,000 employees
