Okay, I've searched for Senior Data Engineer roles in the United States that are full-time, requiring 5+ years of experience, expertise in cloud technologies such as GCP, and strong skills in data engineering and scalable data platforms. I have found 132 positions matching these criteria for you.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Engineer role at Booksy is fundamentally about architecting and maintaining scalable, reliable, and high-quality data infrastructure that empowers the entire organization to make data-driven decisions with confidence. This is not a role simply focused on data pipeline construction but requires deep system design thinking to create robust, automated workflows that ensure data governance and accessibility. The candidate must navigate the uncertainty inherent in a high-growth scale-up environment, where clarity is often nascent and processes may evolve rapidly. Core responsibilities include collaborating across multiple teams to develop self-service data platforms and using cloud-native technologies like GCP's BigQuery, Dataflow, and Pub/Sub to build efficient, maintainable solutions. The role demands a balance of technical expertise—covering backend engineering, cloud infrastructure, CI/CD, and data modeling—and strong interpersonal skills to mentor colleagues and uplift data literacy company-wide. Success will look like delivering scalable data systems that reduce bottlenecks for data consumers, improving data quality metrics, and shaping a data culture that supports rapid innovation and operational decision-making. The ability to synthesize complex, ambiguous challenges and act decisively while fostering collaboration is key.

Company Analysis:

Booksy operates as a fast-growing, innovation-driven scale-up in the global beauty and wellness marketplace, aiming to empower entrepreneurs and customers alike with technology that simplifies scheduling and business growth. This market position demands agility, continuous innovation, and a customer-centric mindset, which resonates throughout the company culture—fast-paced, collaborative, and comfortable with ambiguity and rapid change. The company values inclusivity, creativity, and a strong commitment to employee growth and flexibility, such as remote work opportunities and professional development resources. Given its scale-up nature, hierarchy is likely flat, with high visibility for senior contributors who can drive impact. The Senior Data Engineer role fits strategically into the Data Platform team, directly supporting business growth by enabling data-driven innovation. It’s a pivotal role that interfaces extensively across product, engineering, and business teams, positioning the engineer not just as a technical contributor but as a key enabler of Booksy’s mission to help people thrive. The role’s success will be amplified by embracing the company’s entrepreneurial spirit, continuous learning, and collaborative culture.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to design a data solution in an ambiguous environment. How did you approach the uncertainty and collaborate with stakeholders?
• Behavioral: Describe a situation where you mentored a less experienced team member or a cross-functional colleague on data literacy. What was your approach and the outcome?
• Behavioral: Can you share an experience where you identified and resolved a critical data quality issue before it impacted business decisions?
• Behavioral: Have you ever had to manage competing priorities from different teams while maintaining the integrity of a data platform? How did you handle it?
• Technical: How would you design a scalable data pipeline using GCP services like BigQuery, Dataflow, and Pub/Sub for a high-growth application?
• Technical: Explain your experience with implementing CI/CD and infrastructure as code in the context of data engineering projects. How have these practices improved your workflows?
• Technical: What strategies do you use to ensure robust data governance and quality in large-scale data infrastructures?
• Technical: How familiar are you with dimensional data modeling and schema design? Can you provide an example of how you applied these concepts in your previous work?
• Situational: Imagine that a downstream team reports inconsistent and outdated data affecting their dashboards. How would you investigate and resolve this issue while communicating with stakeholders?
• Situational: You discover that a recently deployed data pipeline is causing excessive costs on cloud resources. How do you approach identifying the root cause and optimizing the system?
• Situational: Suppose leadership requests a rapid rollout of new data features to support an urgent business initiative but the data infrastructure is still maturing. How do you balance speed and quality?
• Company Fit: Why are you interested in working at Booksy, and how do you see your skills contributing to our mission of helping people thrive?
• Company Fit: How do your personal work values align with our culture of inventively solving problems within ambiguity and fast-paced environments?
• Company Fit: What excites you most about the intersection of data engineering and the beauty/wellness marketplace as Booksy operates?
• Questions for Interviewer: How does the Data Platform team currently collaborate with other departments like product and business intelligence at Booksy?
• Questions for Interviewer: What are the biggest data engineering challenges Booksy expects to face in the next 12 months?
• Questions for Interviewer: How does Booksy support ongoing learning and professional development for senior engineers, especially in emerging cloud and data technologies?