10 Years in Data Engineering: Real-World Lessons, Career Growth & AI | Ideas to Impact - 3

Gowtham has spent 10 years in data engineering. He graduated in 2013, moved to Chennai, and worked his way from associate consultant to senior data engineer. Along the way he started making data engineering content, first in Tamil and then in English. In this episode we get into what a data engineer actually does day to day, how it differs from data analyst and data scientist roles, what really happens to your data inside a company, and how AI is changing the job. Gowtham also lays out a practical roadmap for students and freshers, and explains how to talk about your projects so interviewers remember them. What we cover: Data engineer vs data analyst vs data scientist, explained with real examples What a "pipeline" is: ingestion, processing, storage and testing 24x7 streaming jobs, Kafka and on-call support Data privacy, retention and governance in fintech and healthcare How Gowtham uses AI at work, and where a human still has to be in the loop A 3-year roadmap: Linux, SQL, Python, Spark, Databricks and cloud (AWS / GCP) Personal branding, LinkedIn and how to explain projects in interviews 🎤 Guest: Gowtham 🎥 Youtube -    / @dataengineeringtamil   📸 Instagram:   / dataengineeringtamil   🎤 Host: Akhshy | Code With Mapla 🚀 📸 Instagram:   / code.with.mapla   💻 The Dev Labs:   / thedevlabs.io   💼 LinkedIn:   / akhshyganesh   👨‍💻 GitHub: https://github.com/akhshyganesh/ Chapters 0:00 Highlights 1:44 Meet Gowtham: 10 years in data engineering 4:17 From 2013 graduate to Chennai: how it started 6:45 Why he chose data engineering content 9:29 Career path: Hadoop, first AWS project, AstraZeneca, PayPal 13:06 Data engineer vs data analyst vs data scientist 16:35 Beginner to senior: how the role grows 18:26 Where data lives: Europe, US and India rules 21:16 Real example: how data flows at a company like Uber 27:18 What a "pipeline" really means 29:32 24x7 streaming jobs, Kafka and on-call support 35:15 What companies do with your data 39:10 Retention, backups and deleting data 42:27 Side project: building "Table Not Found" with Replit 44:59 Explaining your day-to-day work in interviews 50:21 How Gowtham uses AI at work 55:03 Advice for freshers: picking a tech stack 58:42 Data engineering or full stack? Freelancing for experience 1:00:42 Do you still need DSA and LeetCode? 1:02:37 Build your own roadmap: Hadoop, Spark, Databricks, cloud 1:07:38 Data governance and privacy in fintech and health 1:12:34 Python and SQL on the job 1:15:21 Where AI is allowed: human in the loop 1:15:54 Starting as a data analyst 1:17:26 Interview projects and data volume 1:21:18 Campus talks, LinkedIn and personal branding 1:27:28 A 3-year data engineering roadmap 1:31:05 Projects that impress: real challenges like Black Friday spikes 1:32:30 Rapid fire 1:36:09 What success means now vs 10 years ago 1:38:14 Viewer question: switching from backend to data engineering 1:39:31 Horizontal vs vertical skills: Linux, Airflow, GitHub 1:41:21 AI, layoffs and the future of the job 1:43:19 Wrap-up #DataEngineering #TamilPodcast #DataEngineer #CareerAdvice #BigData #SQL #Spark #AWS #TechCareers #CodeWithMapla #akhshy