Okay, I've searched for full-time Data Analyst roles in Bellevue, WA that require SQL and Python skills, 1+ years of experience, and a Bachelor’s degree. I have found several positions that match your criteria.

Sure. Here's the analysis:

Job Analysis:

The Data Analyst role on Amazon's Transportation Operations Management (TOM) Data Infra team is fundamentally about transforming vast amounts of operational data into actionable insights and scalable data solutions that directly impact the efficiency and growth of Amazon's driver network. This requires not only technical proficiency in SQL, Python, and Amazon's native AWS data tools like Glue and Lambda, but also an ability to design and maintain robust reporting and BI infrastructure with an emphasis on accuracy, timeliness, and customer focus. The analyst will frequently navigate large, complex data sets to design automated processes and visualizations (using QuickSight or similar tools) that offer clear, real-time performance metrics, which is critical in an environment where operational decisions must be data-driven and agile. Success in this role means delivering reliable, scalable reporting tools and data pipelines that improve driver operations, reduce friction, and fuel strategic decisions to manage the explosive demand growth Amazon faces in last-mile delivery. Given the cross-functional nature of the role—working closely with TOM operations and data engineering teams—the ability to communicate findings, document solutions meticulously, and innovate beyond routine analysis is vital. Problem-solving here involves balancing technical complexity, ambiguous data challenges, and evolving business priorities to contribute to long-term infrastructure improvements while meeting urgent business needs.

Company Analysis:

Lensa is a dynamic and innovative company specializing in leveraging machine learning to revolutionize the job search experience by matching candidates and roles with high personal fit, thereby reducing acquisition costs and time. Although this particular role sits within an Amazon team, understanding Lensa's mission helps frame the importance of data-driven precision and user-centric solutions in the ecosystem of talent and operations management. Lensa itself values cutting-edge technology, data science, and a human-first approach, which aligns well with the TOM Data Infra team’s focus on creating meaningful, impactful BI tools that serve both internal stakeholders and end customers (drivers). Lensa's fast growth and global R&D footprint suggest a culture that supports innovation, agility, and continuous improvement—traits essential for the data analyst to thrive. For a candidate, this means working in an environment that encourages technical excellence while maintaining a commitment to practical outcomes that enhance user experiences and business scalability. The analyst role is an individual contributor position with cross-team visibility and a direct line to operational impact. It serves as a strategic hire supporting Amazon’s aggressive growth in delivery logistics, requiring an adaptable mindset and collaborative spirit to align daily tasks with broader company goals of scaling operations and improving efficiency.

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

• Behavioral: Tell me about a time when you had to analyze a large and complex dataset to solve an ambiguous business problem. How did you approach it and what was the outcome?
• Behavioral: Describe an experience where you had to coordinate with multiple teams to deliver a data solution. How did you handle conflicting priorities or communication challenges?
• Behavioral: Share an example of a situation where you discovered discrepancies in a critical report. What steps did you take to resolve the issue and ensure accuracy?
• Behavioral: Give an example of how you have used documentation to share knowledge or ensure maintainability of data solutions in your previous roles.
• Technical: How would you design an ETL pipeline using AWS Glue and Lambda to process incoming delivery driver data efficiently and ensure data quality?
• Technical: Walk me through your experience creating dashboards or reports in Amazon QuickSight or a similar BI tool. What are key considerations to ensure the reports deliver actionable insights?
• Technical: Explain your approach to writing SQL queries for extracting and transforming complex operational datasets. How do you optimize for performance and readability?
• Technical: What data modeling strategies would you apply when designing a data warehouse schema to support transportation operations analytics?
• Situational: Imagine you have a dataset that shows unexpected drops in driver performance metrics, but some of the raw data seems inconsistent. How would you investigate and resolve this?
• Situational: Suppose a leadership stakeholder requests a new report on short notice with incomplete requirements. How would you manage their expectations and deliver the best possible solution quickly?
• Situational: You find that a critical automated report is delivering incorrect figures to stakeholders, but the root cause is not immediately clear. What steps do you take to troubleshoot and fix this?
• Company Fit: Why are you interested in working on Amazon’s Transportation Operations Management team through Lensa, and how do you see your skills contributing to our goals?
• Company Fit: How do you align with Amazon’s customer-obsessed culture and the fast-paced, innovation-driven environment described in this role?
• Company Fit: Lensa emphasizes leveraging technology to improve job search outcomes—how do you think this perspective can inform your work supporting Amazon’s driver network data solutions?
• Questions for Interviewer: Can you describe the TOM Data Infra team’s biggest current challenges and how this role is expected to address them?
• Questions for Interviewer: What does success look like for this Data Analyst in the first 6 months, and how is their impact measured?
• Questions for Interviewer: How does the team balance rapid delivery of data insights with maintaining the long-term scalability and maintainability of data infrastructure?