Suriya Ram Mohan

Right now, it’s not just H1B holders under pressure, the ones really feeling the burn are 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀, 𝗳𝗿𝗲𝘀𝗵 𝗴𝗿𝗮𝗱𝘂𝗮𝘁𝗲𝘀 𝗮𝗻𝗱 𝗮𝗹𝗹 𝘁𝗵𝗲𝘀𝗲 𝗨𝗦 𝗳𝗼𝗹𝗸𝘀 𝗮𝗿𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗯𝗮𝗰𝗸 𝘁𝗼 𝗲𝗮𝘁 𝗜𝗻𝗱𝗶𝗮𝗻 𝗜𝗧 𝗷𝗼𝗯𝘀. So, the bigger hit is actually on:  • Students studying here in US with crazy loan amounts on their head.  • Folks who graduated but still don’t have a job. Many students are already stressed about getting a job, and now this adds on. Dealing with rent, family pressure, loan interest, and living expenses all at once, so just imagine. And don’t think this stops in the US, it’s gonna bounce back to India too. The first impact? 𝗜𝗻𝗱𝗶𝗮𝗻 𝗜𝗧. Bcz the people heading back from the US will be looking at those jobs, and let’s be real, they already have an edge. They’ve worked with 𝗠𝗔𝗔𝗡𝗚/𝗙𝗔𝗔𝗡𝗚, on 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, working on 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵 𝗹𝗶𝗸𝗲 𝗔𝗜/𝗠𝗟. Sure, India has a lot of potential and a big gap to fill in tech. But the real question is, 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘄𝗮𝘃𝗲 𝗼𝗳 𝗿𝗲𝘁𝘂𝗿𝗻𝗲𝗲𝘀? Truth is, 𝘁𝗵𝗲𝘆’𝗹𝗹 𝗲𝗮𝘀𝗶𝗹𝘆 𝗼𝘂𝘁𝗰𝗼𝗺𝗽𝗲𝘁𝗲 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘁𝗮𝗹𝗲𝗻𝘁 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮. Why? Bcz most niche technologies emerge first in the US and only later get outsourced to Indian firms like Infosys or TCS. 𝗦𝗼𝗺𝗲 𝗵𝗮𝗿𝘀𝗵 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀:  • 𝗗𝗼𝗻’𝘁 𝗿𝘂𝘀𝗵 𝘁𝗼 𝘁𝗵𝗲 𝗨𝗦 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄. There are already too many students stuck in the job hunt, some graduated, some still studying, and many more on the way.  • The numbers are insane, 𝗼𝘃𝗲𝗿 𝟭.𝟭𝟮 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 international students in the US (2023-24, record high), 𝟯𝟯𝟭𝗸+ from India—𝟮𝟯% jump in a year. Another report puts Indians at 𝟰𝟮𝟬𝗸 (𝟮𝟳% of all students). Massive pressure on the market.  • A big reason? 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝘄𝗮𝗶𝘃𝗲𝗱 𝗚𝗥𝗘 𝗮𝗻𝗱 𝗧𝗢𝗘𝗙𝗟/𝗜𝗘𝗟𝗧𝗦 during covid, which made it way easier for people to get in US, even if they weren’t really ready for the competitive job market here.  • On top of that, 𝘀𝗶𝗻𝗰𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗯𝗹𝗲𝘄 𝘂𝗽 𝗶𝗻 𝟮𝟬𝟮𝟯, 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗰𝗼𝘂𝗿𝘀𝗲 𝗮𝗻𝗱 𝘄𝗲𝗻𝘁 𝗮𝗹𝗹-𝗶𝗻 𝗼𝗻 𝗔𝗜. Now tell me, 𝗵𝗼𝘄 𝗺𝗮𝗻𝘆 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮 𝗮𝗿𝗲 𝘀𝗲𝗿𝗶𝗼𝘂𝘀𝗹𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜/𝗠𝗟-𝘀𝗸𝗶𝗹𝗹𝗲𝗱 𝗴𝗿𝗮𝗱𝘀? And even if they are, 𝗵𝗼𝘄 𝗺𝗮𝗻𝘆 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗻𝗶𝗰𝗵𝗲 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲, 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝗼𝗿 𝗼𝘁𝗵𝗲𝗿 𝘀𝗲𝗰𝘁𝗼𝗿𝘀 on their own? Not many. So yeah, 𝘁𝗵𝗶𝘀 𝘄𝗮𝘃𝗲 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝗵𝗶𝘁 𝗜𝗻𝗱𝗶𝗮𝗻 𝗜𝗧 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁. And if anyone in India is feeling happy about US layoffs or immigration issues, 𝗷𝘂𝘀𝘁 𝗯𝗲 𝗰𝗮𝗿𝗲𝗳𝘂𝗹, 𝗯𝗰𝘇 𝗶𝗳 𝘁𝗵𝗲𝘀𝗲 𝗨𝗦 𝗳𝗼𝗹𝗸𝘀 𝗮𝗿𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗯𝗮𝗰𝗸... 𝗮𝗻𝗱 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯𝘀.

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Myths that stops Data Scientists moving from SBC to PBC 🚫 1. PBCs only hire from top-tier colleges. I’ve seen people from tier-3 colleges land offers at Swiggy, Fractal, etc. Because strong portfolios and problem-solving skills always speak louder than a college tag. 2. “I’ll prepare for 6 months and then start applying.” Don’t make this mistake. If you’re aiming for a product company, build real-time use cases, keep your fundamentals strong, and apply in parallel. And this one’s my favorite: 3. “SBC experience isn’t valued in PBCs.” Not true at all. Experience is experience. What matters is the tech you’ve worked with, the projects you’ve built, and the impact you can prove. So if you’re in an SBC right now and dreaming of that switch: Stop doubting your background. Start sharpening your skills. Start building. Start applying. And a perfect place to start your journey would be Bosscoder Academy’s Data Science program. Check it here → bcalinks.com/iWliFcQ They’ve helped 2200+ professionals switch into Data & ML roles at top tech companies by offering: ✅Structured curriculum covering ML, Deep Learning & GenAI ✅Industry projects with real world datasets that build your portfolio ✅1:1 mentorship from Data Scientist at top product companies ✅Placement support to help you land your dream data role #myths #datascience #sponsoredpost Image Credit: Google Banana model 🙈

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🚴‍♂️ Excited to share my latest Power BI Project: Chicago Bike Share Analytics Dashboard! 📊 This project is designed with comprehensive analytics covering ride patterns, user behavior, and operational insights across Chicago's bike-sharing network. 🔍 Key Analysis Areas: 1. Ride Volume Analysis – 4.79M total rides with detailed breakdown by member vs casual users (64.4% vs 35.6%). 2. Bike Type Performance – Classic bikes leading usage at 61.25%, followed by electric bikes (37.15%) and electric scooters (1.6%). 3. Temporal Patterns – Average ride length tracking showing member rides (13.04 min) vs casual rides (28.09 min) with seasonal variations. 4. Geographic Distribution – Interactive map visualization of top stations across Chicago with usage density analysis. 5. Weekly & Time Period Trends – Peak usage patterns showing afternoon/evening dominance and weekend vs weekday behaviors. ⭐ What I practiced & applied: - Power Query for data cleaning & transformation across multiple ride datasets. - DAX for KPIs including ride duration calculations, user segmentation metrics, and time intelligence functions. - Data modeling for building relationships between stations, rides, and user types. - Interactive map visualizations and user-friendly navigation in Power BI. Key Insights Discovered: 📈 Average ride length varies significantly: casual users (28.09 min) vs members (13.04 min) 🚲 Classic bikes dominate with 61.25% market share despite electric options ⏰ Clear temporal patterns with peak usage during afternoon and evening hours 🗺️ Geographic clustering shows concentrated usage in downtown Chicago areas. 💡 I'd love to hear your thoughts! What's one additional metric you would track for bike-sharing optimization? #PowerBI #DataAnalytics #UrbanMobility #BikeSharing #DashboardDesign #DataVisualization #TransportationData #ChicagoData

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Week 4 with Dishaa Agarwal at InternSify is all about Excel and Tableau! So, what are the key differences between these two powerful tools? As I go further into my data analytics journey, I’ve come to realize that while Excel and Tableau both work with data, they serve very different purposes. A quick comparison: Excel is perfect for: Data entry Quick calculations In-depth analysis using formulas, pivot tables, and charts Tableau shines in: Data visualization Transforming complex datasets into interactive, beautiful dashboards. Key Differences: User Experience: Excel feels like a spreadsheet; Tableau feels like a storytelling tool. Big Data: Tableau handles large datasets more efficiently. Automation: Tableau auto-updates visuals; Excel often needs manual updates. Purpose: Excel is great for managing and manipulating data. Tableau is best for communicating data. For me, both tools are essential. Learning when and how to use each one is helping me grow into a smarter data analyst. What’s your go-to tool and why? #CareerGrowth #DataAnalyticsJourney #ExcelVsTableau #JDPC

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Sciences Back in 2019, Amazon India used to hire a lot of brilliant scientists. Back then I was jealous of his privilege. I was arguing with my teammate that “No matter how much research you do or you’d like to do, you’d have to eventually push yourself to complete the task at hand. “ I made myself a bit too explicitly vocal, but well… a kid does stupid things when made to feel cornered. He chose a different path. He chose to make fun of my level in subtle ways and went back to his fictional work. I never saw him again in Amazon. He left within the year. I never read his name in any news. Never read any arXiv paper with his name. There are a lot of things you could learn from this story. One could be that just because you started with a bad set of cards, doesn’t mean you’ll keep holding them forever. Another could be, some people conjure up the will to do what’s necessary to climb the ladder; others just keep changing ladders. But the fact remains… That model is still pretty much the same 12 year old kid it was in 2024. The only thing you don’t realise, is that now it’s on cocaine.

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Layoffs = Growth? Bumble just proved it. 🚨 Bumble’s stock soared 26% overnight — not because of a new product, not because of a market expansion… …but because they laid off 30% of their workforce. That’s ~240 jobs cut to save $40 million/year. Harsh? Yes. Strategic? The market sure thinks so. 📉 Bumble’s valuation had plummeted from $7.7B in 2021 to just $538M. Now, with founder Whitney Wolfe Herd back in the CEO seat, they’re rewriting the playbook: → Slim down. → Reinvest in product and tech. → Focus on execution, not just expansion. It’s a cold reminder of where public markets place their bets: efficiency and clarity of vision > bloated org charts. 💬 This isn’t just a Bumble story. It’s a broader signal across tech: In 2025, lean may be the new scale.

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Tiger Analytics is known for asking some solid, real-world Data Engineering questions during interviews. If you're preparing, here’s a quick snapshot of what they typically expect 👇 💰 Role-wise Compensation (Approx.) Data Engineer: ₹15–20 LPA Senior Consultant – Data Engineering: ₹25–30 LPA 📌 Commonly asked technical questions: 1️⃣ How would you build a highly scalable ingestion pipeline for hourly multi-TB parquet files landing in ADLS using ADF + Databricks? Consider partitioning, pruning, and metadata performance tuning. 2️⃣ Explain the internals of OPTIMIZE and ZORDER in Delta Lake. Also discuss the scenarios where running them may not be beneficial. 3️⃣ Write a SQL query to find customers who purchased in 3 or more back-to-back months. 4️⃣ What’s the performance difference between using Delta Lake CDF and MERGE? When is one a better choice over the other? 5️⃣ How does Spark’s Tungsten engine speed up execution? Talk about off-heap memory and whole-stage code generation. 6️⃣ A Databricks job fails intermittently due to data skew. How do you diagnose it and what steps would you take to mitigate it? 7️⃣ What are the best practices for building resilient ADF pipelines? Think retries, monitoring, alerting, and custom logging. 8️⃣ How would you design a watermark strategy in Spark Structured Streaming when events arrive late or out of order? 9️⃣ Delta Live Tables vs traditional Spark notebooks: When should each be used for production-grade ETL? 🔟 Explain SCD Types 1, 2, and 3. How would you efficiently implement Type 2 using Window functions + MERGE? 1️⃣1️⃣ How do you build enterprise data lineage with Purview integrated across ADF and Databricks? 1️⃣2️⃣ Compare Databricks SQL Warehouse with Azure Synapse Dedicated SQL Pool for analytics workloads. 1️⃣3️⃣ Describe a real scenario where you applied data quality checks (schema, nulls, referential integrity) and how you set up automated alerts. 1️⃣4️⃣ Share an experience where you reduced Databricks costs using autoscaling, spot nodes, or better query optimization. 1️⃣5️⃣ Design a near real-time Kafka → Spark Streaming → Delta pipeline. How do you guarantee exactly-once processing and handle replay logic? 👉 If you found this helpful, hit LIKE 👍 and RESHARE 🔁 so more people can benefit from it! 📍 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗱𝗮𝗶𝗹𝘆 𝗦𝗤𝗟, 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗝𝗼𝗯 𝘂𝗽𝗱𝗮𝘁𝗲𝘀: 👉 https://lnkd.in/dhYpfrS7 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗻𝗲𝗿𝘀/𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀. 👉𝗚𝗿𝗮𝗯 𝗶𝘁 𝗵𝗲𝗿𝗲(𝟭𝟬𝟬% 𝗥𝗲𝗳𝘂𝗻𝗱): https://lnkd.in/giHUMNx9

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I recently interviewed with L&T for a 𝟮𝟮 𝗟𝗣𝗔 Data Engineering role… and I lost the offer. But that rejection taught me one lesson: In Data Engineering, clarity beats complexity. Every single time. Here’s how the process went and where I slipped 👇 📍 𝗥𝗼𝘂𝗻𝗱 𝟭: 𝗢𝗻𝗹𝗶𝗻𝗲 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 - SQL queries - Python coding - Scenario-based MCQs on Big Data, ETL, and cloud fundamentals 📍 𝗥𝗼𝘂𝗻𝗱 𝟮: 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 They asked me to design a scalable data pipeline using Kafka → Spark → S3. I explained the flow correctly, but here’s where I went wrong: - I over-engineered parts of the architecture - Jumped too fast into optimization talk - Failed to justify cost efficiency, which mattered a lot for them 📍 𝗥𝗼𝘂𝗻𝗱 𝟯: 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝗶𝗮𝗹 𝗥𝗼𝘂𝗻𝗱 They asked how I would handle Schema Evolution in production. Instead of sharing a real incident from my past project, I gave a textbook answer and that’s where I lost them. This is what I learned: It’s not about throwing buzzwords or complex diagrams. Leaders want clarity, ownership, and why you make certain decisions. Even though I didn’t get the 22 LPA offer → I walked away sharper, clearer, and more interview-ready than before. 💡 If you’re preparing for data engineering roles and want to avoid mistakes like I did, I’d suggest learning with people who can guide you on both tech + storytelling + real-world decision-making. Check Bosscoder Academy Data Program: bcalinks.com/JoeopJB They are offering 25% OFF this month for Data Engineering aspirants, with: 💡 Structured curriculum for SQL, Python, Spark, Kafka, System Design & more. 💡 1:1 Mentorship with MAANG experts 💡 Resume, LinkedIn & Mock Interviews 💡 Real-world case studies (not theory) P.S. Which round do you think is the real deal-breaker Technical or Managerial? #dataengineering #careerswitch #interviewprep #paidpartnership #bigdata

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Machine Learning book review of the month As part of my Master’s coursework at Texas McCombs School of Business subjects like Optimization and Supply Chain Analytics , I learned to start with the math having Constraints, Objective Functions, Gradients and Duality before touching the code. When I received a review copy of Mathematics of Machine Learning by Tivadar it brought back that same mindset of : - Building intuition through linear algebra, calculus, and optimization - Understanding models from a problem-solving lens If you’re building ML models and want to truly understand how they work, this book is gold.

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