- Configured the GCP Cloud Build CI/CD to deploy the Airflow DAGS in Astronomer. Optimized ML Pipeline by segmenting the code into multiple jobs and executing these jobs concurrently on a Dataproc single cluster. This approach significantly reduced costs to 1/6 of the original and decreased the time complexity.- Configured the Cloud Function. Identified the pattern in complex Semi Structured Data (JSON file) and transformed the JSON into BQ loadable format and landed the data in BQ successfully using Cloud Function.- Configured Cloud Run and Cloud Scheduler services to automate the Python codes.

Worked on multiple reporting tasks using Python, SQL, Tableau and Excel that provided business insights to upper management. Worked on ETL Tasks from multiple data sources such as MySQL, MS SQL, PostgreSQL, APIs, Derby, and AWS S3 bucket. Reduced data processing time by 50% by automating data transformation and loading processes. Scheduled and maintained ETL and reporting tasks using task scheduler. Hands on experience in migrating on premise to Google Cloud Platform (GCP) using cloud native tools such as Big Query, Google Cloud Storage. Gathered business requirements from stakeholders and provided automated insights by mail and tableau to upper management on a daily/Weekly/Monthly basis.

• Extensively worked on Oracle database(11g/12c/19c) to develop complex queries based on Business requirements. • Extensive experience in Relational Database Data acquisition, Data wrangling, Data analysis, Query Development, Business process understanding, Deployment. • Developed complex queries using Analytical functions, aggregate functions, Subqueries, appropriate joins without data leakage. • Query Optimized/Performance tuning using Hints, Index, Constraints. • Done potential overpaid case queries and Deployed it to run in schedule basis to identify overpaid claims effectively.• Good Knowledge of Machine Learning Model Pipelines: Data Preprocessing, EDA - Including PCA and t-SNE, Featurization using NLP techniques, Feature Engineering - depend upon case, ML Modelling using Sklearn libraries by utilizing Supervised and Unsupervised algorithms. • Hands-on knowledge on various algorithms like Lin regression, Log regression, SVM, NB, KNN, K means, Decision tree, Random Forest and XGBoost to solve Classification, Regression and Clustering tasks. • Developed my Data science skills by building ML models.