Shubham Wadekar

Shubham WadekarJul 13, 2026

I’ve seen people go from TCS (3.36 LPA) ⇾ Deloitte (24 LPA+) I’ve seen people go from Infosys (3.5 LPA) ⇾ KPMG (27 LPA+) I’ve seen people go from Wipro (4 LPA) ⇾ PwC (29 LPA+) All because they focused on improving their Data Engineering skills: ❌ They didn’t only focus on theory without coding ❌ They didn’t stop after one small demo pipeline ❌ They didn’t waste time on too many certificates Instead, they: ✅ Got strong in basics – SQL, Python, data modeling ✅ Learned how to save cost on cloud tools ✅ Practiced scaling from small data to big data ✅ Built small real projects with free datasets ✅ Practiced fixing errors and making pipelines run faster ✅ Learned how big data tools/cloud really work (Spark, Azure) Because in Data Engineering interviews at Deloitte/KPMG, it’s not just about knowing tools. It’s about how you design, optimize, and explain a solution. 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗻𝗲𝗿𝘀. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲 - https://lnkd.in/gJWSVKNM 𝗠𝗮𝘆 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁 𝗲𝗻𝗱𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆 - AZURE30 / AWS30 / GCP30 only for next 5 people If you've read so far, do LIKE and RESHARE the post👍

Shubham WadekarSep 16, 2026

2022: No salary increase 2023: No salary increase 2024: No salary increase 2025: Switched. 3x salary. 🚀 That’s how it went. • Same work. • Same effort. • Same results. I kept waiting for things to change. and they didn’t. That’s when I realized - effort isn’t enough, direction matters. So I stopped random prep and started following a plan. - Practiced real interview questions. - Revised concepts the right way. - Built consistency with a proper roadmap. And that changed everything. • New company. • New role. • 3x salary. That one switch gave me what 3 years of waiting couldn’t. If you’re in the same place I was, don’t just keep waiting. Start preparing the right way. 𝗞𝗲𝗲𝗽𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗶𝗻 𝗺𝗶𝗻𝗱, 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗻𝗲𝗿𝘀. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲 - https://lnkd.in/gJWSVKNM 𝗗𝗶𝘀𝗰𝗼𝘂𝗻𝘁 𝗲𝗻𝗱𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆 - AZURE30 / AWS30 / GCP30 only for next 5 people If you've read so far, do LIKE and RESHARE the post👍

Shubham WadekarSep 17, 2026

I failed 3 Data Engineering interviews back-to-back. Fractal ❌ Mastercard ❌ PUMA Group ❌ All within 15 days. I had 4+ years of Data Engineering experience. I had built pipelines in production. I thought - “This should be easy" But interviews were a reality check. Same SQL, but the queries were trickier. Same Spark, but questions were deeper. Same resume, but I couldn’t connect it to real problems Same project, but couldn’t explain properly. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟯 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗜 𝘄𝗮𝘀 𝗺𝗮𝗸𝗶𝗻𝗴. 1. I studied tools, not concepts - I could explain what Databricks notebooks are, but not how to optimize jobs with partitions, caching, and cluster configs. - I knew Python syntax, but not how to write clean, modular code for data pipelines with error handling. 2. I assumed “I built it, so I can explain it.” Wrong. I struggled to frame my work. I wasn’t telling the story of → problem → design choice → impact. 3. I ignored fundamentals - SQL window functions, data partitioning, file formats, distributed joins That’s where most interview questions came from. 𝗪𝗵𝗮𝘁 𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗶𝗻 𝗺𝘆 𝗽𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻: • I took my own pipelines and asked myself “Why did I design it this way?” • I wrote project explanations in a Q&A format, like I’m the interviewer. • I re-learned fundamentals: SQL, Spark internals, data modeling - not just tools. Next interview? Cleared ✅️ 𝗠𝗼𝗿𝗮𝗹? You don’t fail because you don’t know Spark or ADF. You fail because you don’t know how to explain your work like a data engineer. Everything comes from practice, reflection, and getting your hands dirty. 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗻𝗲𝗿𝘀. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲 - https://lnkd.in/gJWSVKNM 𝗠𝗮𝗿𝗰𝗵 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁 𝗲𝗻𝗱𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆 - AZURE30 / AWS30 / GCP30 only for next 5 people If you've read so far, do LIKE and RESHARE the post👍