Okay, I've searched for Senior Data Engineer roles in Seattle, WA within the Financial Services industry, requiring 5+ years of experience, with expertise in Snowflake and Python. I have found 72 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Engineer, Finance role at SoFi is fundamentally centered on architecting and maintaining robust data infrastructure that directly supports the company's financial reporting and business intelligence capabilities. This means the candidate will build and optimize scalable ETL/ELT pipelines and manage the Snowflake data warehouse to ensure accurate, timely, and reliable financial data flows. Beyond technical execution, this role requires close collaboration with finance teams—FP&A, Accounting, Strategic Finance—and BI analysts to translate complex business requirements into efficient data models and pipelines, supporting critical business decisions. The skillset reflects a blend of deep technical expertise in cloud data warehousing, Python, SQL, and orchestration tools like Airflow, alongside strong communication and problem-solving skills to bridge technical solutions with finance-oriented business problems. Autonomy and judgment are essential; the engineer must navigate ambiguous data challenges, troubleshoot emergent issues prior to financial reporting deadlines, and uphold data governance and compliance in a highly sensitive domain. Success in the first year likely means delivering reliable, scalable data solutions that improve self-service analytics, reduce manual data handling, and engender trust among finance stakeholders by ensuring data accuracy and security. The role balances detailed, hands-on engineering with strategic partnership, requiring someone who thrives both as an individual contributor and a collaborative team member.

Company Analysis:

SoFi positions itself as a forward-thinking, tech-driven financial services and national banking company focused on reshaping personal finance through innovative, mobile-first solutions. As a mission-driven organization thriving amid an industry in transformation, SoFi values impact, agility, and member-centric innovation. For a senior data engineering hire in finance, this context suggests a fast-paced, evolving environment where technology directly empowers meaningful financial outcomes for millions of users. The culture likely stresses continuous learning, proactive problem-solving, and cross-functional collaboration, especially between tech and finance. Given SoFi's growth focus and expanding product suite, the Finance BI & Systems team serves as a strategic linchpin providing data insights that drive finance strategy and operational excellence. This role is probably high-visibility, partnering with senior finance leadership and technical peers, demanding excellent communication and influence skills. The job's strategic importance lies in scaling and automating financial data systems to support dynamic business needs, regulatory standards, and innovation objectives. A candidate who aligns with SoFi’s mission for financial independence, embraces technology-driven change, and thrives in a collaborative, high-impact culture will be well-positioned for long-term success and meaningful contribution.

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

• Behavioral: Tell me about a time when you had to design or optimize a complex ETL/ELT pipeline under tight deadlines. How did you ensure quality and performance?
• Behavioral: Describe an experience where you collaborated closely with non-technical finance stakeholders to understand and translate their data needs. How did you bridge the communication gap?
• Behavioral: Give an example of when you identified a critical data quality issue affecting financial reporting. How did you troubleshoot and resolve it?
• Behavioral: How do you maintain attention to detail while juggling multiple data projects and priorities in a fast-paced environment?
• Technical: Can you walk me through your experience managing data warehouses in Snowflake? How do you optimize data models specifically for financial reporting workloads?
• Technical: Explain your approach to building scalable and maintainable ETL/ELT pipelines. What tools and best practices have you found most effective in complex financial environments?
• Technical: How do you implement and enforce data governance, security, and compliance practices within cloud data platforms, especially when dealing with sensitive financial data?
• Technical: Describe your experience with orchestration tools like Airflow and dbt. How do you use them to automate and monitor workflows reliably?
• Situational: Suppose a key financial data source you rely on suddenly changes format or schema without prior notice. How would you respond and mitigate disruption?
• Situational: Imagine you receive conflicting requests from multiple finance stakeholders with overlapping deadlines. How do you prioritize and manage deliverables effectively?
• Situational: If you find a persistent data discrepancy that impacts forecasting models and strategic finance insights, but the root cause is buried in upstream systems outside your control, how would you proceed?
• Company Fit/Motivation: What attracts you to SoFi as a company, and how does our mission to help members achieve financial independence resonate with your professional goals?
• Company Fit/Motivation: How do you see your role as a Senior Data Engineer contributing to the transformation of financial services at a tech-forward company like SoFi?
• Company Fit/Motivation: SoFi emphasizes collaboration between technical and non-technical teams. How do you ensure your technical expertise creates value across diverse partners?
• Questions for Interviewer: Could you describe the typical projects and challenges the Finance BI & Systems team faces in the next 6-12 months?
• Questions for Interviewer: How does SoFi balance fast innovation with data governance and regulatory compliance within the Finance data ecosystem?
• Questions for Interviewer: What opportunities exist for professional development or growth for data engineers within SoFi, especially in financial domains?