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app/blog/faang-interview-guide/page.mdx

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+<Cover
+  src="https://cdn.cosmos.so/affd4b79-e848-4dfd-bd42-5f2c4a847365?format=jpeg"
+  alt="Image from the movie Alien - from cosmos.com"
+  caption="cosmos.com"
+/>
+
+# FAANG Data Engineering Interview Guide
+
+A comprehensive guide to help you prepare for data engineering interviews at top tech companies like Facebook, Amazon, Apple, Netflix, and Google.
+
+---
+
+## Introduction
+
+Set the stage:  
+- What is this guide about?  
+- Why are FAANG interviews unique?  
+- Who is this guide for?
+
+---
+
+## Interview Process Overview
+
+Summarize the general interview flow across FAANG companies:  
+- Application and recruiter screen  
+- Technical phone screens  
+- Take-home assignments (if applicable)  
+- On-site/virtual onsite interviews  
+- Final rounds and offer stage
+
+---
+
+## Core Skills to Master
+
+List the essential areas you’ll be tested on:
+
+### 1. SQL & Data Manipulation
+- Complex joins, aggregations, window functions  
+- Common questions and practice tips
+
+### 2. Data Modeling
+- Star vs. snowflake schema  
+- Normalization vs. denormalization  
+- Designing scalable data warehouses
+
+### 3. ETL and Pipelines
+- Building resilient pipelines  
+- Workflow orchestration tools (Airflow, AWS Step Functions)  
+- Real-time vs batch processing
+
+### 4. Big Data Ecosystem
+- Hadoop, Spark, Hive, Presto  
+- Hands-on experience and where to practice
+
+### 5. Python or Scala for Data Engineering
+- Writing clean, testable data pipelines  
+- Pandas vs PySpark – when to use what  
+- Basic coding problems and data structures
+
+### 6. System Design for Data Engineers
+- Designing a logging pipeline, recommendation system, etc.  
+- Trade-offs: latency, throughput, fault tolerance  
+- Data lake vs. data warehouse
+
+### 7. Behavioral Interviews
+- STAR format  
+- Leadership principles (especially for Amazon)  
+- Cross-functional collaboration examples
+
+---
+
+## Company-Specific Tips
+
+### Amazon
+- Focus on SQL, behavioral alignment with leadership principles  
+- Redshift, Glue, Lambda knowledge is helpful
+
+### Google
+- More emphasis on algorithms and coding  
+- BigQuery, Dataflow, and systems thinking
+
+### Facebook (Meta)
+- Expect deep SQL, data pipeline design, and product sense  
+- Communication and collaboration are key
+
+### Apple
+- Focus on clean, maintainable code and end-to-end pipeline knowledge  
+- Emphasis on craftsmanship and reliability
+
+### Netflix
+- Strong emphasis on data architecture and ownership  
+- Python, Spark, and business alignment are valued
+
+---
+
+## Study Resources
+
+A few resources you can recommend or personally found helpful:
+
+- [LeetCode – SQL & Easy Python Problems](https://leetcode.com)  
+- [Interview Query](https://interviewquery.com)  
+- [Designing Data-Intensive Applications by Martin Kleppmann]  
+- [Data Engineering Zoomcamp](https://github.com/DataTalksClub/data-engineering-zoomcamp)  
+- [Apache Spark and the Unified Analytics Engine (Databricks)]  
+
+---
+
+## Practice Questions
+
+Add a few sample questions or link to a list:
+
+- Write a SQL query to find the second highest salary.
+- Design a data pipeline that ingests streaming data and aggregates metrics every 10 minutes.
+- What’s the difference between row-based and columnar storage?
+
+---
+
+## Final Tips
+
+Wrap-up advice for candidates:
+
+- Practice explaining your projects clearly  
+- Mock interviews with peers or platforms like Pramp  
+- Document and reflect after every interview
+
+---
+
+## Good Luck!
+
+You’ve got this! Preparation, consistency, and clarity go a long way in landing your dream data engineering job at FAANG.
+
+---
+
+## Stay Connected
+
+Feel free to reach out for questions, mock interviews, or collaboration!  
+[LinkedIn](#) • [Email](#) • [GitHub](#)

app/blog/my-journey-at-amazon/page.mdx

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+<Cover
+  src="https://cdn.cosmos.so/affd4b79-e848-4dfd-bd42-5f2c4a847365?format=jpeg"
+  alt="Image from the movie Alien - from cosmos.com"
+  caption="cosmos.com"
+/>
+
+# My Experience Working in Data at Amazon
+
+## Introduction
+
+A brief overview of my role at Amazon and what this post will cover.  
+(Example: what team you were on, the kind of work you did, and your general goals in writing this.)
+
+---
+
+## Getting Started at Amazon
+
+Talk about how you joined, what onboarding was like, and your first impressions.  
+- Team structure  
+- Initial projects  
+- Culture and expectations
+
+---
+
+## The Day-to-Day Work
+
+Describe what a typical day looked like.  
+- Tools and tech stack  
+- Meetings and collaboration  
+- Challenges and rewards
+
+---
+
+## Key Projects and Impact
+
+Highlight some important projects you worked on.  
+- Problem statements  
+- Technologies used  
+- Outcomes and business impact
+
+---
+
+## What I Learned
+
+Reflect on the technical and non-technical lessons.  
+- Skills gained (e.g., SQL optimization, data pipeline design)  
+- Working with cross-functional teams  
+- Communication and decision-making at scale
+
+---
+
+## The Amazon Culture
+
+Share your take on the work environment and company values.  
+- Leadership Principles in action  
+- Internal mobility  
+- Performance reviews and feedback
+
+---
+
+## Advice for Others Interested in Data Roles at Amazon
+
+Tips for aspiring Amazonians or those interested in data roles at big tech companies.  
+- Interview prep  
+- Skill-building recommendations  
+- What to expect
+
+---
+
+## Conclusion
+
+A wrap-up of your time at Amazon and what you’re doing now (or