Okay, I've searched for Data Engineer roles in Bellevue, WA, within finance or business intelligence sectors, requiring 3+ years of experience, expertise in SQL and AWS technologies, for full-time positions. I have found 142 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Engineer role within the ISCAP Finance Business Process Intelligence at Amazon Web Services is fundamentally about architecting and maintaining a sophisticated, scalable, and secure data infrastructure that supports large-scale financial and supply chain analytics. The candidate is expected not only to manage massive datasets but also to enable actionable insights through metrics, reports, and dashboards that directly influence business strategy. The responsibilities point to a blend of technical rigor—designing data models, building ETL pipelines, fine-tuning SQL queries—and strategic thinking, such as triaging options in ambiguous contexts and aligning data solutions with broader organizational goals. The emphasis on AWS technologies reflects the need for deep cloud proficiency to leverage native tools for performance, scalability, and security. Success in this role means delivering reliable, well-optimized data assets and fostering a self-service analytics environment that empowers finance stakeholders. The ideal candidate must possess both strong engineering capabilities and the ability to contextualize data challenges within business and operational frameworks, ensuring decisions are data-driven but pragmatically sound.

Company Analysis:

Amazon Web Services is a dominant and rapidly evolving leader in cloud computing, providing infrastructure that fundamentally reshapes how businesses operate globally. This role sits squarely within a culture that values innovation, scale, and agility, emphasizing both technical excellence and customer obsession. Given AWS's pioneering status and continuous growth, the environment is fast-paced and driven by continuous improvement and operational excellence. The team likely operates with significant autonomy but also with high accountability and visibility, engaging cross-functionally with product managers and business stakeholders. Amazon's culture prioritizes data-driven decision-making, inclusion, and adaptability – qualities essential for this Data Engineer to thrive. Furthermore, the strategic placement of this role within finance and supply chain analytics highlights AWS’s commitment to leveraging data as a competitive and operational advantage. The candidate should expect to be part of a high-impact team that enables crucial business insights, contributing directly to Amazon’s scalable, efficient cloud ecosystem.

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

• Behavioral: Tell me about a time when you had to design a data pipeline that handled a large volume of financial data. What challenges did you face and how did you overcome them?
• Behavioral: Describe an instance when you identified the root cause of a complex data-related issue impacting business metrics. How did you approach the problem?
• Behavioral: Give an example of a time you had to work closely with non-technical finance stakeholders. How did you ensure the data solution met their needs?
• Behavioral: Share a situation where you had to make a difficult decision in a high-ambiguity environment. How did you balance quantitative analysis and business judgment?
• Technical: How would you design and implement an ETL pipeline on AWS for processing millions of daily transactional records securely and efficiently?
• Technical: Explain your experience with AWS services such as Redshift, Glue, and Lambda, particularly in the context of data engineering. How do you choose which services to use?
• Technical: How do you approach performance tuning for complex SQL queries and data retrieval within a cloud-based data warehouse?
• Technical: What strategies and tools do you use to ensure data integrity, validation, and documentation in a large-scale data environment?
• Situational: Imagine you discover significant latency caused by an inefficient data pipeline just before a critical business reporting deadline. How would you address this?
• Situational: You are tasked with automating a manual data reporting process for finance stakeholders who have limited technical skills. What steps would you take?
• Situational: Suppose you need to select between multiple data stores (relational vs non-relational) for a new dataset with evolving schema needs. How would you evaluate and decide?
• Company Fit: Why are you interested in working at Amazon Web Services, specifically in this data engineering role supporting finance and supply chain analytics?
• Company Fit: How do you align with Amazon’s leadership principles, particularly 'Customer Obsession' and 'Dive Deep', as they relate to this position?
• Company Fit: Amazon values a culture of continuous learning and innovation. How do you keep pace with rapidly evolving data technologies and incorporate them into your work?
• Candidate Questions: What are the biggest data challenges currently facing the AISSCF Analytics team, and how does this role contribute to solving them?
• Candidate Questions: How does the data engineering team collaborate with finance and product management to prioritize and deliver impactful projects?
• Candidate Questions: What opportunities exist for professional growth and skill development within the AWS data engineering teams?