Sri Kavya Email & Phone Number

• Experience in data transformations using Map-Reduce, HIVE for different file formats.• Involved in converting Hive/SQL queries into transformations using Python• Performed complex joins on tables in hive with various optimization techniques• Designed 3NF data models for ODS, OLTP systems and dimensional data models using Star and Snowflake Schemas• Worked on Snowflake environment to remove redundancy and load real time data from various data sources into HDFS using Kafka• Developed data warehouse model in Snowflake for over 100 datasets• Designing and implementing a fully operational production grade large scale data solution on Snowflake Data Warehouse• Work with structured/semi-structured data ingestion and processing on AWS using S3, Python. Migrate on-premises big data workloads to AWS• Designed the data aggregations on Hive for ETL processing on Amazon EMR to process data as per business requirement• Involved in migration of data from existing RDBMS to Hadoop using Sqoop for processing data, evaluate performance of various algorithms/models/strategies based on real-world data sets• Designed and configured Kafka cluster to accommodate heavy throughput of 1 million messages per second. Used Kafka producer 0.6.3 API's to produce messages• Extract Real time feed using Kafka and Spark Streaming and convert it to RDD and process data in the form of Data Frame and save the data as Parquet format in HDFS• Used Kafka and Kafka brokers, initiated the spark context and processed live streaming information with RDD and Used Kafka to load data into HDFS and NoSQL databases.• Subscribing the Kafka topic with Kafka consumer client and process the events in real time using spark.• Development of Spark structured streaming to read the data from Kafka in real time and batch modes, apply different mode of Change data captures (CDCs) and then load the data into Hive

Greenwood Village, Co, Us

• Designed and developed spark jobs for performing ETL on large volumes of medical membership and claims data• Created Airflow Scheduling scripts in Python• Involved in importing the real time data to Hadoop using Kafka and implemented the Oozie job for daily• Developed applications of Machine Learning, Statistical Analysis, and Data Visualizations with challenging data Processing problems• Compiled data from various sources public and private databases to perform complex analysis and data manipulation for actionable results.• Designed and developed Natural Language Processing models for sentiment analysis.• Worked on Natural Language Processing with NLTK module of python for application development for automated customer response.• Demonstrated experience in design and implementation of Statistical models, Predictive models, enterprise data model, metadata solution and data lifecycle management in both RDBMS, Big Data environments.• Used predictive modeling with tools in SAS, SPSS, and Python.• Applied concepts of probability, distribution, and statistical inference on given dataset to unearth interesting findings through these of comparison, T-test, F-test, R-squared, P-value etc.• Applied linear regression, multiple regression, ordinary least square method, mean-variance, the theory of large numbers, logistic regression, dummy variable, residuals, Poisson distribution, Bayes, Naive Bayes, fitting function etc. to data with help of Scikit, SciPy, NumPy and Pandas module of Python.• Applied clustering algorithms i.e. Hierarchical, K-means with help of Scikit and SciPy.• Developed visualizations and dashboards using ggplot2, Tableau• Worked on development of data warehouse, Data Lake and ETL systems using relational and non-relational tools like SQL, No SQL.• Built and analyzed datasets using R, SAS, MATLAB, and Python (in decreasing order of usage).

North Chicago, Illinois, Us

• Worked with Hadoop eco system covering HDFS, HBase, YARN and MapReduce• Worked with Oozie Workflow Engine in running workflow jobs with actions that run Hadoop MapReduce, Hive, Spark jobs• Performed Data Mapping, Data design (Data Modeling) to integrate data across multiple databases in to EDW• Responsible for design and development of advanced Python programs to prepare transform and harmonize data sets in preparation for modeling• Hands on experience on Hadoop /Big Data related technology experience in Storage, Querying, Processing, and analysis of data• Developed Spark/Scala, Python for regular expression (RegEx) project in Hadoop/Hive environment for big data resources.• Automated the monthly data validation process to validate the data for nulls and duplicates and created reports and metrics to share it with business teams• Used clustering techniques like K-means to identify outliers and to classify unlabeled data• Data gathering, data cleaning and data wrangling performed using Python• Transformed raw data into actionable insights by incorporating various statistical techniques, data mining, data cleaning, data quality, integrity utilizing Python (Scikit-Learn, NumPy, Pandas, and Matplotlib) and SQL• Calculated errors using various machine learning algorithms such as Linear Regression, Ridge Regression, Lasso Regression, Elastic net regression, KNN, Decision Tree Regressor, SVM, Bagging Decision Trees, Random Forest, AdaBoost, and XGBoost. Chose best model eventually based on MAE• Experimented with Ensemble methods to increase accuracy of training model with different Bagging and Boosting methods• Identified target groups by conducting Segmentation analysis using Clustering techniques like K-means• Conducted model optimization and comparison using stepwise function based on AIC value• Used cross-validation to test models with different batches of data to optimize models and prevent over fitting

Hyderabad, Andhra Pradesh, In

• Used Pandas, NumPy, Seaborn, SciPy, Matplotlib, Scikit-learn, and NLTK in Python for developing various machine learning algorithms and utilized machine learning algorithms such as linear regression, multivariate regression, naive Bayes, Random Forests, K-means, &KNN for data analysis.• Demonstrated experience in design and implementation of Statistical models, Predictive models, enterprise data model, metadata solution and data lifecycle management in both RDBMS, Big Data environments.• Utilized domain knowledge and application portfolio knowledge to play a key role in defining the future state of large, business technology programs.• Created and modified several database objects such as Tables, Views, Indexes, Constraints, Stored procedures, Packages, Functions and Triggers using SQL and PL/SQL.• Created large datasets by combining individual datasets using various inner and outer joins in SQL and also performed dataset sorting and merging.• Created ecosystem models (e.g. conceptual, logical, physical, canonical) that are required for supporting services within the enterprise data architecture (conceptual data model for defining the major subject areas used, ecosystem logical model for defining standard business meaning for entities and fields, and an ecosystem canonical model for defining the standard messages and formats to be used in data integration services throughout the ecosystem)• Developed Linux Shell scripts by using NZSQL/NZLOAD utilities to load data from flat files to Netezza database.• Designed and implemented system architecture for Amazon EC2 based cloud-hosted solution for the client.• Tested Complex ETL Mappings and Sessions based on business user requirements and business rules to load data from source flat files and RDBMS tables to target tables.