Data engineering in the realm of social networks presents unique challenges and opportunities.  From the impact of network effects on experiments to the vast amounts of user-generated data, a successful data engineer in this space needs a specialized skill set.

If you're preparing for a data engineering interview, here's a breakdown of key concepts and common questions you're likely to encounter:

A/B Testing in the Social Media Context

• Understanding Network Effects in Social Media A/B Testing: User interactions on social networks can create ripple effects that impact A/B test results. Be prepared to discuss how you'd account for this in your experimental design.
• Reference Materials:  Explore resources like the OkCupid engineering blog or Quora discussions for real-world examples of network effects in testing.

Metrics and Analysis for Social Networks

• Key Metrics for Facebook and Social Networks:  Discuss the core metrics social networks track, such as Daily Active Users (DAUs), engagement metrics (likes, comments, shares), and ad performance indicators.
• Troubleshooting Metric Drops in Social Networks: If a key metric drops unexpectedly, be ready to outline your investigation process.  Consider factors like data quality issues, seasonality, algorithm changes, or  competitor activity.‍

Problem-Solving in Social Network Data Engineering

• Detecting Fake News on Social Platforms:  Understand the strategies used to identify fake news, including machine learning models, content analysis, and user flagging systems.
• Social Network Ad Revenue Analysis:  If ad revenue declines, be prepared to describe how you'd analyze the situation. This could involve breaking down revenue by source, examining audience segments, and looking at competitive trends.

Experimentation and Success Measurement

• Evaluating Social Media Experiments and Feature Success:  Analyze scenarios where experimental results might be mixed, such as decreased likes but increased comments and time spent.  Focus on the company's goals and prioritize relevant metrics. Define KPIs aligned with the feature's objectives.
• Defining and Monitoring Social App Health:   Explain how "health" relates to KPIs like retention, engagement, crashes, and error rates.  Outline a monitoring strategy for these metrics.
• Conflicting Metrics:  When one metric improves and another worsens, investigate potential trade-offs, user segmentation, and unintended consequences of a feature change.

Let me know if you have any specific interview experiences or additional questions you'd like to discuss!