The SaaSocalypse: Hype, Reality, and What Data Teams Should Actually Do
$285 billion wiped from SaaS valuations in 48 hours — which threats are genuine and which are overblown?
Helping data teams, engineering leaders, and ambitious builders turn complex data challenges into innovative, scalable solutions.
Tech and people leader, writer, mentor, coach and community-minded builder with deep exposure across the data & AI lifecycle.
Data and engineering leadership
Mentoring Club rating
Degrees spanning engineering, computer science, and management
Mission: make technical growth more human and actionable
Leadership in big data, broad software lifecycle experience, strong communication skills, and an interest in technology, entrepreneurship, writing, design, and photography.
B.Tech, Anna University
M.Tech in CSE, JNTU Hyderabad
MBA in Information Management, SMU
Berlin leadership role in big data and AI platforms
A recent public mentoring review describes: "Excellent engineering leader and an empathetic mentor who listens carefully before advising".
Takeaways from the GoDataFest panel discussion on responsible AI adoption and stakeholder trust.
Why AI projects stall at pilot stage — trust, explainability, governance, and what it takes to scale ethically.
Key insights and summary from the executive roundtable on data, AI strategy, and leadership.
Staying ahead in the ever-evolving tech landscape, common solutions to challenges tech leaders face and more.
A candid conversation on data platform leadership, engineering culture, and AI-first strategy.
Organising Engineering Management kata sessions for Berlin's growing tech community.
Building a Strong Team Culture: Essential Practices for Success.
A renowned monthly newsletter about data engineering and all things data.
A weekly newsletter about Data & AI.
$285 billion wiped from SaaS valuations in 48 hours — which threats are genuine and which are overblown?
Gen AI handed everyone a power tool — what skills remain defensible in an AI-augmented era?
Benchmarks say 90% accuracy. Your production warehouse might say otherwise.
Hidden constraints and failure modes — Delta clustering limits, Iceberg manifest bloat.
A comprehensive comparison with a decision framework for when each approach delivers value.
A framework for evaluating build-vs-buy decisions amid AI tool proliferation and decision fatigue.
Designed around practical outcomes: career clarity, technical leadership, AI/data platform judgment, and communication confidence.
Clarify role direction, leadership readiness, interview narratives, and next-step execution.
Talk through architecture tradeoffs, stakeholder alignment, roadmap pressure, and team rituals.
Turn complex technical context into clear decisions, stronger feedback, and calmer leadership.
Prototype flow with session selection, availability, contact details, and confirmation state.
“Chozhan is an excellent engineering leader and a very empathetic mentor. During our session, he really put himself in my shoes, listened carefully, and asked thoughtful questions before sharing his advice. He created a safe, open space to talk honestly about my challenges and goals, and his guidance was both practical and reassuring. I left the session with much more clarity about my next steps and felt genuinely supported and motivated in my career journey.”
- M Ajay“I had a wonderful discussion with Mr. Chozhan. He was kind enough to listen attentively and answer all my questions. He provided valuable guidance on navigating my current situation, and his insights were incredibly helpful. Our conversation was truly enjoyable, and I gained a deeper understanding of the job market, interview processes, industry standards, and key expectations in the data engineering field. This discussion gave me much-needed clarity, helping me identify the right opportunities to pursue. I sincerely appreciate his time and effort.”
- M Murugan"The session was extremely insightful and helped me gain much-needed clarity on my career direction. We discussed the different paths within the data domain, including Data Engineering and related disciplines, and how my current experience can be effectively positioned. I particularly appreciated the structured approach to evaluating which path best aligns with my strengths and long-term goals. The guidance provided was practical, relevant, and has given me a clear direction on my next steps."
- A Kothapalli