Okay, I've searched for full-time Data Engineer roles in analytics based in Carson City, NV, requiring a Bachelor's degree in Computer Science or related fields with 5+ years of experience, and with remote work options. I have found 23 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Data Engineer, Analytics role at Meta is fundamentally designed to build and maintain the robust data infrastructure that empowers decision-making across multiple product domains. Beyond merely coding and maintaining ETL pipelines, this role demands a strategic focus on data modeling, warehousing, and governance to ensure the integrity, availability, and security of data. The candidate will collaborate across functions — product managers, data scientists, and other engineers — requiring strong communication skills to translate complex data requirements into scalable solutions. Success in this role means not just technical proficiency but also the ability to anticipate data needs, optimize pipelines, and creatively solve integration challenges across varied data sources, including structured and unstructured data. The heavy emphasis on SLAs, security models, and data quality reflects Meta’s need for rigorous, reliable data systems underpinning high-stakes products and features. The role requires a depth of experience—five years or more—with big data ecosystems, advanced ETL design, Python programming, and strong SQL skills, ensuring the engineer can confidently produce impactful analytics solutions with minimal supervision while navigating ambiguity and balancing competing priorities.

Company Analysis:

Lensa, as the platform sourcing candidates for Meta, operates in a cutting-edge intersection of machine learning and career technology, striving to transform job search by improving matching algorithms and candidate insights. Meta itself is a technology powerhouse moving rapidly into new frontiers like augmented and virtual reality, signaling a culture that prizes innovation, scale, and relentless technical excellence. For a Data Engineer, this environment means working within a highly ambitious, fast-evolving company where data drives not only product features but the company’s strategic vision toward the metaverse and beyond. Meta’s culture is likely fast-paced and cross-functional, emphasizing autonomy, creativity, and influence, with the candidate expected to shape data solutions that impact billions globally. The role is strategically vital, as reliable, timely data feeds into product decisions and user experience at an immense scale, so visibility and collaboration with leadership and multiple teams are expected. Telecommuting flexibility and a diverse, inclusive workforce reflect Meta’s modern, adaptable work philosophy, requiring the candidate to be a proactive communicator and self-starter to thrive and grow within the company’s mission.

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

• Tell me about a time when you had to design an ETL pipeline to integrate data from multiple, diverse sources. What challenges did you face and how did you overcome them?
• Describe an experience where you identified and resolved a significant data quality issue in a warehouse environment.
• Can you share an example of how you influenced product or cross-functional teams through your data engineering work?
• How do you prioritize tasks and manage SLAs when handling multiple data sets with competing deadlines?
• Walk me through your approach to designing a data warehouse schema for a new product with complex analytical needs.
• How have you implemented data security models in your past roles, especially to comply with privacy requirements?
• What tools and frameworks have you found most effective for optimizing big data pipelines and why?
• Explain how you would collaborate with data scientists and product managers to understand and translate their data requirements into actionable models.
• Imagine you discover inconsistent data is affecting dashboard insights just before a critical leadership review. How would you handle this situation?
• Suppose a new product domain requests data support, but existing pipelines are overloaded. How would you approach balancing resources and delivering for this new request?
• Why are you interested in working with Meta in the Data Engineer, Analytics role, given our focus on social technology evolution and immersive experiences?
• How do you see your skills and values aligning with Meta’s mission to build the next generation of social technology?
• What interests you most about engineering data infrastructure in a fast-paced, scale-oriented environment like Meta?
• What questions do you have about the data team’s collaboration style and the company's expectations for innovation in data engineering?
• Could you share how performance and success are measured for data engineers on this team, and what growth opportunities exist beyond this role?