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AI or No AI ...........🔥 Data Engineering is here to stay.

I see so many engineers wanting to build a career in Data Engineering but not having a clue as to where to start.

Very fortunate to have got some time from Sitaram Akella (https://www.linkedin.com/in/sitaramakella/), a Data Engineering Leader with extensive experience in working at Microsoft and Salesforce, for hosting the "AI Masterclass: Becoming an AI-Ready Data Engineer in 2026" session.

This is a MUST ATTEND if you wish to either explore a career as a Data Engineer or want to just understand what Data Engineering is all about.

📅 14 February
⏰ 11:00 AM – 12:00 PM
📍 Hybrid Mode (Online | Hyderabad)

🔗 Register: https://luma.com/9ch4imli

In this AI Masterclass, we will cover the following topics.
1️⃣ The Reality Check
👉 “Is Traditional Data Engineering Enough Anymore?”

  1. How AI is reshaping data platforms
  2. Why ETL-only skills are becoming commoditized
  3. The rise of AI-native companies
  4. What changed after LLMs went mainstream

🔥“Why some data engineers will earn 2x in 2026 — and others will struggle.”

2️⃣ The AI-Driven Data Stack (15–20 mins)
👉 From Pipelines to AI-Ready Platforms

  1. Modern data stack vs AI stack
  2. Data Lakes → Lakehouse → Vector Databases
  3. Streaming + Real-time AI inference
  4. Data quality for ML systems
  5. Feature stores and why they matter

Key tools that we will discuss:

  1. Spark / Databricks
  2. Kafka
  3. Airflow
  4. Snowflake
  5. Vector DBs
  6. MLOps pipelines

3️⃣ What “AI-Ready” Actually Means (20 mins)
👉 The Skill Upgrade Blueprint

  1. Break it down into 5 pillars:
  2. Advanced Data Modeling for AI workloads
  3. Distributed Systems Understanding
  4. ML + AI Fundamentals for Data Engineers
  5. LLM Data Preparation & Retrieval Pipelines
  6. MLOps + Observability
  7. What recruiters actually care about

4️⃣ Career Roadmap for 2026 (15–20 mins)
👉 How to Position Yourself Strategically

  1. Entry-level vs mid-level strategy
  2. Transitioning from Backend / BI / Analytics
  3. Certifications: useful or waste?
  4. How to build a standout portfolio

What hiring managers secretly evaluate
💡 Includes real resume mistakes. 💡 Includes real interview signals.

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