Okay, I've searched for Big Data Engineer roles in Rapid City, SD, specifically within the financial industry, with a salary of at least $90,000, and requiring a Bachelor's degree and 3+ years of experience in data engineering. I found 27 positions that match your criteria.

Sure. Here's the analysis:

Job Analysis:

The Big Data Engineer role at Synchrony is fundamentally about enabling data-driven decision-making by constructing and maintaining Synchrony's Enterprise Data Lake. This position requires a deep understanding of the data ingestion, standardization, and curation processes to deliver high-quality data that meets the nuanced requirements of various business units. The engineer will need to effectively integrate diverse internal and external data sources, focusing on compliance with Data Quality and Data Governance standards. The complexity of tasks revolves around developing analytic applications using technologies like Hadoop and Spark, necessitating both analytical and programming skills. Successful performance in this role means collaborating effectively across teams, demonstrating an ability to tackle challenges in data accuracy and processing efficiency while adapting to a fast-paced Agile environment. Achieving the expectations set forth in the job description hinges not only on technical know-how but also on the ability to communicate clearly with various stakeholders, ensuring that data solutions are aligned with business objectives.

Company Analysis:

Synchrony operates in the consumer financing industry, solidly positioned as a provider that aims to improve financial health in people's everyday lives. The company promotes a mission-driven culture, highlighting accessibility and support for millions of consumers and businesses alike. This focus creates an environment where the Big Data Engineer role is crucial, as the quality and governance of data directly influence decision-making processes across the organization. A candidate for this role should expect a collaborative, Agile atmosphere that values flexibility and team cohesion. Additionally, the presence of multiple functional teams implies that the incumbent will need to engage cross-functionally, impacting overall business strategies and outcomes. The innovative, supportive nature of Synchrony's culture indicates that the company values ongoing development and responsiveness to change, making it essential for the engineer to possess a willingness to learn and adapt. A clear understanding of Synchrony’s business practices and being data-driven aligned with the company's future innovations is key to succeeding in this role.

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

• Can you describe a project where you successfully integrated multiple data sources? What challenges did you face, and how did you overcome them?
• Tell me about a time when you identified a data quality issue. What steps did you take to resolve it?
• Describe your experience working in an Agile environment. How did you adapt your work style to fit Agile practices?
• How do you ensure that your technical documentation is clear and useful for your team members?
• What specific experience do you have with Hadoop ecosystems, and how have you utilized it in past projects?
• Can you give an example of a complex SQL query you wrote? What problem were you trying to solve?
• How would you approach developing a data quality rule for a data set you are unfamiliar with?
• Imagine a scenario where your data ingestion process fails intermittently. How would you diagnose and address the issue?
• Why do you think data governance is critical in a consumer finance context, and how would you contribute to that within this role?
• What interests you most about working at Synchrony, especially in the context of improving financial health for consumers?
• How do you see your skills aligning with Synchrony’s mission to support small and mid-sized businesses?
• Can you ask about the current technology stack the team uses for data engineering?
• What opportunities do you see at Synchrony for innovation within the data engineering space?