Okay, I've searched for Data Engineer, Product Analytics roles in New York, NY, requiring 7+ years of experience with SQL and ETL skills, full-time positions in the internet industry. I have found 126 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Engineer role at Meta is fundamentally centered on architecting and delivering large-scale, high-impact data solutions that power product analytics across Meta’s diverse suite of applications with billions of users. Beyond routine data engineering, the role demands ownership of data architecture design, stringent data governance, and creative problem solving to build scalable ETL pipelines and sophisticated data models that enable product teams to identify growth and engagement opportunities. The candidate must be comfortable navigating complex technical challenges related to data volume, quality, security, and integration from diverse sources, all within an ecosystem where real-time precision and efficiency are crucial. Collaboration is key, as this role acts as a bridge between engineers, data scientists, and product managers, requiring not just technical proficiency but the ability to influence through data storytelling and strategic insights. Success means not only delivering clean, accessible data but becoming a trusted partner who drives product decisions and uncovers new avenues for user and business value. The required 7+ years of experience and mastery of SQL, ETL, and programming reflect the advanced technical depth needed to operate at Meta’s scale, while the role also expects leadership in mentoring and shaping best practices, indicating a senior-level strategic contributor position.

Company Analysis:

Meta sits at the pinnacle of the internet industry as a global innovator and disruptor, building products used by over three billion people. This positions the company as a high-velocity, impact-driven environment where data-driven innovation shapes everything from product development to user engagement. The culture likely values bias for action, continuous learning, and cross-functional collaboration in a mission to connect and empower communities worldwide. Though the job is routed through Lensa, which itself champions machine learning to optimize hiring, the Meta role signifies a shift toward working within one of the most complex data ecosystems on the planet. The company’s scale and diversity of products mean that a data engineer here has extraordinary visibility and opportunity to influence global products. The strategic purpose of this hire aligns with sustaining Meta’s edge in leveraging massive data assets, driving growth and product excellence. For a candidate, success at Meta will require resilience in ambiguity, agility in communication, and the capacity to manage and innovate within vast, intricate data systems, while embracing Meta’s culture of inclusion, impact, and forward-thinking technology deployment.

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 extremely large and complex datasets. What challenges did you face and how did you overcome them?
• Behavioral: Describe a situation where you had to influence product or engineering teams using data-driven insights. How did you communicate your recommendations to ensure buy-in?
• Behavioral: Give an example of a time when you identified a data quality or security issue in a production system. What steps did you take to address it?
• Behavioral: How have you approached mentoring junior team members in previous roles? Can you share a specific example where your feedback helped improve a colleague's performance?
• Technical: Walk me through your process for designing an ETL pipeline that integrates both structured and unstructured data sources at scale.
• Technical: How do you ensure data security and compliance when architecting data models, particularly when dealing with sensitive user information?
• Technical: Which SQL optimization techniques do you typically use in handling complex queries for large datasets? Can you illustrate with an example?
• Technical: What tools and frameworks have you used to visualize data insights for product teams? How do you tailor these visualizations to different audiences?
• Situational: Imagine a cross-functional team disagrees about the validity of a data-derived insight that you delivered. How would you handle that disagreement?
• Situational: If you discover that a critical data pipeline is running inefficiently and causing delays in product analytics reports, what immediate and long-term actions would you take?
• Situational: How would you prioritize multiple data engineering projects when product managers request urgent support for competing high-impact features?
• Company Fit: What motivates you to work at Meta, especially in a data engineering role that interfaces extensively with product and growth teams?
• Company Fit: How do your personal values and work style align with Meta’s mission to connect the world and innovate at an incredible scale?
• Company Fit: Given the rapidly evolving landscape of social platforms and immersive technologies, how do you stay current with trends and adapt your data engineering approach accordingly?
• Questions for Interviewer: How does Meta balance innovation and data governance/security in its product analytics workflows?
• Questions for Interviewer: What opportunities are there for professional growth and leadership development within the data engineering teams at Meta?
• Questions for Interviewer: Can you describe the typical cross-functional collaboration process between data engineers, data scientists, and product managers on major projects here?