Shylendra Sai Bangaru

Skills

Programming Languages : Python (Pandas, NumPy, SciPy, Matplotlib, Scikit-Learn, TensorFlow, PyTorch), R, SQL, JavaScript, Scala, Java
Data Analytics/Modeling : Jupyter Notebook, Google Colab, Apache Zeppelin, SAS, SPSS, MATLAB
Data Visualization : Tableau, Power BI, Looker, D3.js, Seaborn, ggplot2, Matplotlib, Plotly
Big Data Tools : Apache Hadoop, Apache Spark, Apache Flink, Databricks, Apache Kafka, Apache Hive, Presto
ETL Tools : Apache Nifi, Apache Airflow, Talend, Informatica PowerCenter, AWS Glue
OLAP Tools : Apache Kylin, Microsoft Analysis Services, Oracle OLAP, Amazon Redshift
Databases : PostgreSQL, MySQL, MongoDB, Apache Cassandra, Oracle, Microsoft SQL Server, GoogleBigQuery, Amazon DynamoDB, Snowflake
Cloud Platforms : AWS (S3, EC2, RDS, Lambda), Google Cloud Platform (BigQuery, Dataflow, AI Platform), Microsoft Azure (Synapse, HDInsight)
DevOps Tools : Docker, Kubernetes, Jenkins, Git, Terraform, Ansible
Project Methodologies : Agile, Scrum, Kanban, Waterfall
Machine Learning Libraries : TensorFlow, PyTorch, Keras, XGBoost, LightGBM, H2O.ai
AI & Deep Learning : TensorFlow, Keras, PyTorch, FastAI, OpenCV
Data Warehousing Tools : Snowflake, Redshift, Teradata, Google BigQuery, Azure Synapse

About

Experienced Data Architect and Analyst with over 9 years of expertise in data architecture, modeling, and engineering across both OLTP and OLAP environments. Adept at designing scalable data solutions using modern architectures, including star and snowflake schemas, and managing complex data ecosystems with proficiency in dimensional and relational modeling.

Skilled in leveraging tools like Erwin, Power Designer, and ER Studio for building conceptual, logical, and physical models, with hands-on experience across Big Data platforms such as Hadoop, Spark, Hive, Pig, and PySpark. Proven ability to implement data governance, profiling, cleansing, and ETL strategies using Informatica, PowerCenter, SSIS, and custom automation via UNIX and Perl scripting.

Well-versed in cloud data services on AWS (Redshift, S3, EMR) and Azure (Data Lake), with strong command of SQL and procedural languages across Oracle, Teradata, Netezza, and DB2. Passionate about delivering high-quality, actionable insights using predictive analytics and machine learning models (Random Forest, SVM, Logistic Regression, Neural Networks) in Python and R.

Experienced in managing complete data science project lifecycles—from acquisition and transformation to modeling and visualization—using tools like Tableau and libraries like scikit-learn, caret, dplyr, and ggplot2. Strong advocate of SDLC, Agile, and industry best practices for building high-impact, reliable data systems that support business growth and innovation.