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Eat, Sleep, Code, Repeat
👨‍💻
Eat, Sleep, Code, Repeat
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hamzanasirr/README.md

Hi, I’m Hamza Nasir 👋

Databricks Apache Spark Apache Kafka Snowflake AWS GenAI

AI-Augmented Senior Data Engineering Consultant
I help teams build scalable, resilient data platforms — and future-proof them with GenAI-driven workflows.

Portfolio 🔗 -> Portfolio.BigDataLad.com

🚀 What I Do

I work with fast-moving teams to design, optimize, and modernize data systems that actually hold up in production.

My focus sits at the intersection of:

  • High-scale data engineering
  • Cost-aware cloud architectures
  • AI-augmented pipelines & agent-driven workflows
  • Real-world reliability (not slideware)

I care less about buzzwords — and more about impact, stability, and outcomes.

🧠 Areas of Expertise

Core Data Stack

  • Databricks · Apache Spark · Kafka
  • Airflow · Custom orchestration patterns
  • AWS (S3, data & compute primitives)

Data Engineering

  • Large-scale batch & streaming pipelines
  • Chunked processing for fault-tolerance & resilience
  • Custom CDC implementations
  • Performance optimization & cost reduction

GenAI & AI-Augmented Systems

  • LLM-powered data workflows
  • RAG architectures
  • Agent-based automation
  • AI-assisted analytics & engineering productivity

📈 Selected Impact

  • Built and optimized a complex Gene Expression Pipeline processing data in intelligent chunks — dramatically reducing failure rates and improving recoverability at scale.
  • Designed custom CDC mechanisms for non-trivial data sources where off-the-shelf tools fell short.
  • Helped teams move from “it works locally” to production-grade, observable, and scalable systems.

🎤 Speaking, Teaching & Community

I actively share real-world lessons from the field:

  • Panel Speaker — AWS Cloud Club (2025)
  • Panel Speaker — AI Nexus
  • Panel Speaker — Cloud Nexus
  • Talk — The Future of Data Engineering in the Age of AI Agents (Dice Analytics, Islamabad)
  • Workshop — Data Lakes at COMSATS University, Islamabad

I also mentor engineers transitioning into senior and consulting roles.

✍️ Writing & Thought Leadership

I write about data engineering, architecture decisions, and GenAI’s practical impact on the field:

👉 https://bigdatalad.com

Expect fewer platitudes — more patterns, trade-offs, and honest takes.

🌍 How I Work

  • Consulting-first mindset
  • Outcome-driven, not tool-driven
  • Comfortable collaborating across time zones
  • Experience working with globally distributed teams

🤝 Let’s Connect

If you’re building or re-thinking a data platform — or exploring how AI can actually help your data teams:

Strong data systems age well. Fragile ones don’t. I help teams build the former.

Popular repositories Loading

  1. Credit-Card-Fraud-Detection-using-Machine-Learning Credit-Card-Fraud-Detection-using-Machine-Learning Public

    In this kernel, we are going to predict whether a credit card is fraud or not using Machine Learning.

    Jupyter Notebook 27 20

  2. Predicting-Graduate-Admissions-using-Machine-Learning-in-Python Predicting-Graduate-Admissions-using-Machine-Learning-in-Python Public

    Predicting if a candidate student will get admission or not in a particular university using Machine Learning algorithms in Python.

    Jupyter Notebook 5 3

  3. claude-learning-resources claude-learning-resources Public

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  4. facebook-user-behavior-prediction facebook-user-behavior-prediction Public

    Let's say we want to identify certain groups of Facebook users based on their behavior. Some facebook users might not react on the posts. Some people may 'like' react more and use other reactions l…

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  5. Prediction-of-Train-Ticket-Prices-in-Spain-using-Supervised-Machine-Learning Prediction-of-Train-Ticket-Prices-in-Spain-using-Supervised-Machine-Learning Public

    Prediction of Train Ticket Prices in Spain using Supervised Machine Learning Algorithms and then comparing R-Squared coefficient to select the most well-suited model for deployment.

    Jupyter Notebook 2 3

  6. Exploratory-Data-Analysis-on-Bank-Customer-Churn-data Exploratory-Data-Analysis-on-Bank-Customer-Churn-data Public

    Performed EDA on the bank's customer churn data and tried to get some insights.

    Jupyter Notebook 1 2