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Chandratejk/README.md

👋Hello!

I'm Chandratej Kurella

Data Analyst | Cloud Data Engineer | GCP & AWS Specialist

A cloud native data professional with experience transforming fragmented datasets into real-time, business-critical intelligence. I don’t just build pipelines -- I engineer ecosystems that move data with precision, power, and purpose.

Armed with a Master’s in Business Analytics from California State University, East Bay, I’ve architected scalable solutions across cloud first and data intensive environments. My toolbox spans AWS,GCP, Apache Spark, Kafka, Informatica, Power BI and more., but tools are just the medium. The mission? Making data work harder, faster, and smarter.

I don’t stop at pipelines, I bring the story to life through visualization. Whether in Power BI, Looker Studio, or Tableau, I design dashboards that are crisp, intuitive, and tuned to how business leaders actually think. For me, reporting isn’t about charts on a screen. it is about translating raw numbers into a visual narrative that makes patterns undeniable and decisions obvious.

Whether dissecting healthcare claims, tracking fraud patterns in banking, or decoding user behavior across digital platforms, I apply analytical thinking to surface insights that drive strategy. My work blends SQL-driven exploration with intuitive dashboarding and stakeholder-first storytelling — turning complex datasets into decisions that matter.

From crafting CDC frameworks that sync global systems in real-time, to building metadata-driven ETL pipelines that think for themselves, I design with scalability, resilience, and clarity in mind. Whether it's infrastructure telemetry or digital product analytics, I deliver clean, reliable pipelines that teams can trust and business can build on.

If you're building for scale and ready to make data a competitive edge — I'm all in.

🏅 Certifications

📊 Microsoft Certified Power BI Data Analyst
☁️ AWS Certified Data Engineer – Associate
🎓 Google Cloud Certified Professional Data Engineer

🛠 Tech Stack & Tools

💻 Languages: Python (Pandas, PySpark, NumPy), R, SQL
⚙️ Data Engineering: Dataflow, Glue, Dataform, Apache Spark, Kafka, Informatica CDI, DBT
☁️ Cloud: AWS (Glue, S3, Lambda, Redshift), GCP (BigQuery, Dataflow, Pub/Sub, Cloud Composer)
🔁 ETL & Workflow Orchestration: Airflow, Composer, Spring Boot, Jenkins, Terraform, GitHub Actions
📊 Visualization & Reporting: Power BI, Tableau, Kibana, Excel macros, Power Automate (RPA)
📐 Data Modeling: Star/Snowflake schemas, CDC, relational & NoSQL integration
🔐 Governance & Compliance: IAM, DLP, Data Catalog, PII/GDPR compliance

📬 Let’s Connect

📧 [email protected] | 📍United States

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  1. BigData-Visibility-Analytics-with-Apache-Spark BigData-Visibility-Analytics-with-Apache-Spark Public

    Python

  2. Credit-Risk-Analysis-Using-German-Credit-Dataset Credit-Risk-Analysis-Using-German-Credit-Dataset Public

    Jupyter Notebook

  3. ETL-Pipeline-Automation-for-Banking-Data ETL-Pipeline-Automation-for-Banking-Data Public

    Jupyter Notebook

  4. EV-Purchase-Decision-using-Machine-Learning-Model EV-Purchase-Decision-using-Machine-Learning-Model Public

    Jupyter Notebook

  5. Regression-with-the-Abalone-Dataset-using-Machine-Learning Regression-with-the-Abalone-Dataset-using-Machine-Learning Public

    Jupyter Notebook

  6. Obesity-Risk-Prediction-using-Machine-Learning Obesity-Risk-Prediction-using-Machine-Learning Public

    Jupyter Notebook