Sajal G.

The Avg. Comp for Meta E5 Level in India is over 75 Lakhs+, including bonuses and stocks, it goes over 1Cr+, and this is just at the Sr. Software Engineer level (5 YOE+) The Avg. Comp for Google E5 Level in India is over 60 lakhs+, including bonuses and stocks, it also goes over 1Cr+, this is the Sr. Software Engineer level (5 YOE+) If I were targeting Sr. Roles are Meta, Google, and Microsoft with my 5+ YOE, this is 1 cheat I would 100% use, created from my own experience. How to use this cheatsheet in an interview? Think in three quick steps: 1. Listen to the problem and label the pattern Example in your head: This is mostly read-heavy with some writes or This is real-time fanout, lots of live updates 2. Pick the closest pattern from the list below 3. Use the lines like this when you speak – First arrow → what kind of systems does this pattern fit – Second arrow → what really matters here – Third arrow → which tools or building blocks to mention – Fourth arrow → how you might summarise it to the interviewer This will help to structure your thinking. By the way, if you are preparing for DSA or system design, try our mock interview tool on Layrs for free:http://layrs.me/interviews 1. Read heavy systems → Fits: feeds, product pages, profiles, dashboards → Focus: fast reads, acceptable staleness, low DB pressure → Tools: cache in front of DB, read replicas, search index, background jobs → How to say it: "This is read heavy. I would put a cache and maybe a search index in front of the main DB, and refresh it with background jobs so most requests never hit the core database." 2. Write heavy, event-driven systems → Fits: logs, clickstream, payments events, audit trails → Focus: durable writes, ordering, handling spikes → Tools: queues or streams, consumers, batching, idempotent handlers, dead letter queue → How to say it: "Here writes are the main thing. I would land everything on a queue or stream first, then process with consumers that are idempotent and can batch work to the database." 3. Real-time fanout systems → Fits: chat, live scores, delivery tracking, notifications → Focus: low latency push, many consumers, avoid constant polling → Tools: websockets or SSE, pub or sub layer, in memory cache, fanout service → How to say it: "This needs real time fanout. I would keep a websocket layer or similar, push updates through a pub or sub channel, and avoid going to the database for every small update." 4. Batch and analytics systems → Fits: reports, BI dashboards, recommendation training pipelines → Focus: huge volume, freshness in minutes or hours, cheap scans → Tools: data lake or warehouse, ETL or ELT pipeline, columnar formats, pre aggregated tables → How to say it: "This is batch and analytics. I would separate the OLTP database from the analytics store, load data in batches, and build pre-aggregated tables for the dashboards." Check the comments as well, please…

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I just published a detailed deep dive on designing a Commerce Intelligence Platform for India’s fragmented quick-commerce ecosystem. Brands today sell across Zepto, Blinkit, Flipkart Minutes, Amazon — but struggle to answer: • What is my true category market size? • What is my brand share vs competitors? • Why did my organic rank drop? • Which new launches are gaining momentum? This article covers: ✔ Full database schema (entity → fact → mart layers) ✔ Rank-based market size estimation using Zipf’s Law ✔ Sponsored vs organic separation logic ✔ Tier-based accuracy system (100% accuracy for top SKUs) ✔ Microservices architecture ✔ LLM integration for classification, entity resolution & root-cause analysis ✔ SQL queries for every major business insight It’s a complete master architecture — end to end. Would love feedback from product, data, and commerce folks. #Ecommerce #QuickCommerce #SystemDesign #DataEngineering #AI #ProductArchitecture

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Oracle reportedly planning major layoffs. And honestly, this should make us rethink how we talk about careers in tech. Thousands of employees could be impacted, reportedly including people in India, as Oracle continues to invest heavily in AI. What worries me isn’t just the layoffs. It’s the advice we keep giving people: “Just upskill.” “Learn AI.” “Stay interview-ready.” But what happens when someone already has the skills, experience, and knowledge that got them hired in the first place — and still loses their job because of restructuring, cost-cutting, or a strategic shift? We need to have an honest conversation about this. In India, when people earn well, the system is quick to tax their income. But when they suddenly lose their job, where is the support? And to all the students entering tech: Don’t enter this field believing that a good degree + good skills = permanent job security. Tech can offer incredible opportunities, but it can also come with layoffs, corporate politics, uncertainty, and constant pressure to remain employable. Upskilling is important. But it cannot be the entire solution. Companies, governments, and the industry need to think about what happens to people after the paycheck stops. Because behind every layoff number is a person, a family, and years of experience. Maybe it’s time we stopped treating layoffs as an individual failure and started discussing them as a systemic problem. #Layoffs #Tech #Oracle #AI #FutureOfWork #Career #Technology #India

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