Okay, I've searched for Senior Data Engineer roles in San Jose, CA, with full-time positions, requiring 10+ years of experience, and skills in PySpark and cloud platforms. I have found 37 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Senior Data Engineer role is fundamentally about enabling an organization's data-driven decision-making through the design, implementation, and maintenance of scalable, efficient, and reliable data pipelines and dashboards. The candidate is expected not just to write code but to architect robust data workflows that smoothly handle petabyte-scale data and integrate with external APIs, reflecting the operational complexity in a mature SaaS environment. The emphasis on cloud-native applications (AWS, Azure, or GCP), orchestration tools like Airflow or AWS Glue, and modern data paradigms such as Medallion Architecture and Delta Live Tables, highlights the need for someone who is deeply versed in contemporary big data ecosystems and cloud cost optimization. Success here demands a professional with at least a decade of experience, capable of reducing technical debt and automating workflows to improve reliability and maintainability. Moreover, the role implies constant collaboration with business stakeholders, requiring strong communication and translation of complex technical solutions into business value. Data governance, privacy, and compliance are critical given the likely sensitive data context, stressing an engineer who is detail-oriented and strategically aware of regulatory nuances. Performance tuning and proactive anomaly detection show that the job requires vigilance to system health and end-user experience. Ultimately, this role requires a self-starter with strong problem-solving skills and a proactive mindset who can thrive in a hybrid setting, delivering high-impact solutions rapidly while adapting to evolving technological landscapes.

Company Analysis:

SGA is a boutique technology and resource solutions provider with a strong emphasis on personal, tailored service as a certified women-owned business. Its market position as a provider of contingent staffing and managed services to a diverse array of clients reflects both stability and niche specialization rather than pure product innovation, yet serving clients who are leaders in their fields. This suggests the candidate will join a company that values customer service, integrity, and employee development, and expects its consultants to internalize these values. The culture appears collaborative and supportive, focused on quality and transparency, which will encourage ownership and accountability in this role. As a strategic partner to clients, SGA fosters long-term success over quick wins, meaning the Data Engineer must be mindful of both technical excellence and client-specific contextual demands. Organizationally, this role likely reports within a Data Science Engineering team but also interfaces broadly across business and technical teams, offering high visibility and requiring cross-functional skill. The hire appears critical to fulfilling complex and large-scale data engineering needs for premier SaaS clients, aligning with SGA's mission to solve 'big IT problems with a more personal approach.' Overall, this positioning implies that successful candidates should not only be technically skilled and autonomous but also customer-focused and culturally aligned with a boutique, values-driven environment that prizes adaptability and proactive service.

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 under tight deadlines but still ensured scalability and maintainability. How did you balance speed and quality?
• Behavioral: Describe a situation where you identified technical debt in a data workflow. What steps did you take to reduce it and what was the outcome?
• Behavioral: Give an example of how you have collaborated with non-technical stakeholders to translate their business requirements into technical data solutions.
• Behavioral: How have you demonstrated proactivity in detecting and resolving data anomalies or system performance issues in a past role?
• Technical: Walk me through how you would design and implement an ETL pipeline using PySpark and Airflow to process petabyte-scale datasets efficiently.
• Technical: What strategies do you use to optimize cloud costs when deploying data pipelines on platforms like AWS or Azure?
• Technical: Explain your experience with Medallion Architecture and Delta Live Tables. How have these frameworks improved your data workflows?
• Technical: How do you approach performance tuning for complex dashboards built on Tableau or Power BI?
• Situational: Imagine you receive inconsistent data from an external RESTful API integrated into your pipeline. How would you identify and resolve the issue without impacting downstream analytics?
• Situational: If a newly deployed data pipeline causes unexpected delays in workflow orchestration, how would you troubleshoot and communicate the problem to both technical and non-technical teams?
• Situational: Suppose you discover a breach of data privacy compliance in an existing workflow. What immediate and long-term steps would you take to rectify the situation?
• Company Fit: What interests you most about working with SGA and its approach to technology consulting as a boutique, women-owned business?
• Company Fit: How do your personal values align with SGA’s commitment to customer service, integrity, and employee development?
• Company Fit: Given SGA’s emphasis on solving big IT problems through personal attention, how would you deliver value beyond just technical execution in this role?
• Candidate Questions: Can you describe the typical project lifecycle for a Data Engineer on the client teams supported by SGA?
• Candidate Questions: How does SGA support ongoing professional development for its consultants in rapidly evolving technology areas like big data and cloud platforms?
• Candidate Questions: What are the key challenges your SaaS clients face with their data infrastructure, and how is this role designed to address those?