Kartikay Raniwala
Summary
Dynamic professional with near 3 years experience in intersection of finance, analytics, and data engineering, seeking challenging opportunities to leverage a comprehensive skill set for innovative and efficient data-driven solutions.
Overview
3
3
years of professional experience
1
1
Certification
Work History
Data Engineer
RAZORPAY
Bangalore
05.2021 - Current
- Developed and built a real-time anomaly detection data pipeline using a ML time series forecasting model which alerted the stakeholders with a severity level of it whenever there is a drop in transaction rate in comparison to the prediction generated by the model which decreased the problem resolution time and helped in providing a better customer experience
- Developed and built multiple automated ETL/ELT data pipelines with data ingestion capability from multiple disparate sources using Amazon Redshift, S3, Spectrum, Python, Airflow
- Maintained, Scaled and supported existing and new ETL/ELT data pipelines while ensuring proper storing of both raw and processed data
- Built 100+ SQL complex data models using DBT and orchestrated them using Airflow DAGs to transform raw data into analytics-ready tables in an automated fashion which reduced data preparation time and also provided monitoring of end-to-end workflows
- Collaborated with cross-functional teams to understand business requirements and translated them into scalable and efficient data pipelines.
Education
Post-Graduation Program in Data Science and Engineering -
Great Lakes Institute of Management
01.2020
BBA (Finance) -
Bharati Vidyapeeth Institute of Management and Entrepreneurship Development
01.2019
Skills
- Programming Languages: Python, SQL, Jinja templating
- Database: MySQL, PostgreSQL
- Cloud Platform: AWS
- Data Warehousing: Amazon RedshiftSpectrum
- ETL/ELT Tools: Airflow, DBT, Glue, Lambda
- Version Control: GIT
- ML(Supervised & Unsupervised): Linear and Logistic Regression (R-Square, AUC, Confusion Matrix, Classification Report etc), Decision Trees (depth, splits, leaves etc hyper parameters), Naïve Bayes, Bagging, Boosting (AdaBoost, Gradient Boosting, etc), Random Forest, KNN, Clustering (K-Means, Agglomerative), Principal Component Analysis
- Other Tools: Tableau, Advanced Excel, Word, Powerpoint
Capstone Project
About: 10-year dataset for 130 US Hospitals predicting chances of readmission to suggest alternative treatment methods. Dataset being ~100,000 50 with 23 key features on medicines handling more than 200,000 null values.
Tools and techniques: Python, EDA, Feature Engineering & Selection (Lasso), Ridge, Decision Trees, Random Forest, KNN, AdaBoost, LightGBM, XGBoost, Bagging Classifier, Hyper-Parameter Tuning (RandomizedSearchCV), Model Selection based on Recall.
Certification
Cyber Security (Grade – ‘A’)- Institute of Management and Entrepreneurship Development
Timeline
Data Engineer
RAZORPAY
05.2021 - Current
Post-Graduation Program in Data Science and Engineering -
Great Lakes Institute of Management
BBA (Finance) -
Bharati Vidyapeeth Institute of Management and Entrepreneurship Development
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