SUMMARY

• 8 years of IT experience in a variety of industries working on Big Data technology using technologies such as Cloudera and Hortonworks distributions.
• Hadoop working environment includes Hadoop, Spark, MapReduce, Kafka, Hive, Ambari, Sqoop, HBase, and Impala.
• Fluent programming experience with Scala, Java, Python, SQL, T - SQL, R.
• Hands-on experience in developing and deploying enterprise-based applications using major Hadoop ecosystem components like MapReduce, YARN, Hive, HBase, Flume, Sqoop, Spark MLlib, Spark GraphX, Spark SQL, Kafka.
• Adept at configuring and installing Hadoop/Spark Ecosystem Components.
• Proficient with Spark Core, Spark SQL, Spark MLlib, Spark GraphX and Spark Streaming for processing and transforming complex data using in-memory computing capabilities written in Scala. Worked with Spark to improve efficiency of existing algorithms using Spark Context, Spark SQL, Spark MLlib, Data Frame, Pair RDD's and Spark YARN.
• Experience in application of various data sources like Oracle SE2, SQL Server, Flat Files and Unstructured files into a data warehouse.
• Able to use Sqoop to migrate data between RDBMS, NoSQL databases and HDFS.
• Experience in Extraction, Transformation and Loading (ETL) data from various sources into Data Warehouses, as well as data processing like collecting, aggregating and moving data from various sources using Apache Flume, Kafka, PowerBI and Microsoft SSIS.
• Hands-on experience with Hadoop architecture and various components such as Hadoop File System HDFS, Job Tracker, Task Tracker, Name Node, Data Node and Hadoop MapReduce programming.
• Comprehensive experience in developing simple to complex Map reduce and Streaming jobs using Scala and Java for data cleansing, filtering and data aggregation. Also possess detailed knowledge of MapReduce framework.
• Used IDEs like Eclipse, IntelliJ IDE, PyCharm IDE, Notepad ++, and Visual Studio for development.
• Seasoned practice in Machine Learning algorithms and Predictive Modeling such as Linear Regression, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, KNN, Neural Networks, and K-means Clustering.
• Ample knowledge of data architecture including data ingestion pipeline design, Hadoop/Spark architecture, data modeling, data mining, machine learning and advanced data processing.
• Experience working with NoSQL databases like Cassandra and HBase and developed real-time read/write access to very large datasets via HBase.
• Developed Spark Applications that can handle data from various RDBMS (MySQL, Oracle Database) and Streaming sources.
• Proficient SQL experience in querying, data extraction/transformations and developing queries for a wide range of applications.
• Capable of processing large sets (Gigabytes) of structured, semi-structured or unstructured data.
• Experience in analyzing data using HiveQL, Pig, HBase and custom MapReduce programs in Java 8.
• Experience working with GitHub/Git 2.12 source and version control systems.
• Strong in core Java concepts including Object-Oriented Design (OOD) and Java components like Collections Framework, Exception handling, I/O system.

TECHNICAL SKILLS

Hadoop/Big Data Technologies: HDFS, Hive, Pig, Sqoop, Yarn, Spark, Spark SQL, Kafka

Hadoop Distributions: Horton works and Cloudera Hadoop

Languages: C, C++, Python, Scala, UNIX Shell Script, COBOL, SQL and PL/SQL

Tools: Teradata SQL Assistant, Pycharm, Autosys

Operating Systems: Linux, Unix, ZOS and Windows

Databases: Teradata, Oracle 9i/10g, DB2, SQL Server, MySQL 4.x/5.x

ETL Tools: IBM InfoSphere Information Server V8, V8.5 & V9.1

Reporting: Tableau

PROFESSIONAL EXPERIENCE

Data Engineer

Confidential, Boston, MA

Responsibilities:

• Collaborated with Business Analysts, SMEs across departments to gather business requirements, and identify workable items for further development.
• Partnered with ETL developers to ensure that data is well cleaned and the data warehouse is up-to-date for reporting purpose by Pig.
• Selected and generated data into CSV files and stored them into AWS S3 by using AWS EC2 and then structured and stored in AWS Redshift.
• Processed some simple statistical analysis of data profiling like cancel rate, var, skew, kurt of trades, and runs of each stock every day group by 1 min, 5 min, and 15 min.
• Used PySpark and Pandas to calculate the moving average and RSI score of the stocks and generated them into data warehouse.
• Exploring with Spark to improve the performance and optimization of the existing algorithms in Hadoop using Spark context, Spark-SQL, postgreSQL, Data Frame, OpenShift, Talend, pair RDD's
• Involved in integration of Hadoop cluster with spark engine to perform BATCH and GRAPHX operations.
• Performed data preprocessing and feature engineering for further predictive analytics using Python Pandas.
• Developed and validated machine learning models including Ridge and Lasso regression for predicting total amount of trade.
• Boosted the performance of regression models by applying polynomial transformation and feature selection and used those methods to select stocks.
• Generated report on predictive analytics using Python and Tableau including visualizing model performance and prediction results.
• Utilized Agile and Scrum methodology for team and project management.
• Used Git for version control with colleagues.

Environment: Spark (PySpark, SparkSQL, SparkMLIib), Python 3.x (Scikit-learn, Numpy, Pandas), Tableau 10.1, GitHub, AWS EMR/EC2/S3/Redshift, and Pig.

Data Engineer

Confidential, Eagan, MN

Responsibilities:

• Implemented Apache Airflow for authoring, scheduling and monitoring Data Pipelines
• Designed several DAGs (Directed Acyclic Graph) for automating ETL pipelines
• Performed data extraction, transformation, loading, and integration in data warehouse, operational data stores and master data management
• Strong understanding of AWS components such as EC2 and S3
• Performed Data Migration to GCP
• Responsible for data services and data movement infrastructures
• Experienced in ETL concepts, building ETL solutions and Data modeling
• Worked on architecting the ETL transformation layers and writing spark jobs to do the processing.
• Aggregated daily sales team updates to send report to executives and to organize jobs running on Spark clusters
• Loaded application analytics data into data warehouse in regular intervals of time
• Designed & build infrastructure for the Google Cloud environment from scratch
• Experienced in fact dimensional modeling (Star schema, Snowflake schema), transactional modeling and SCD (Slowly changing dimension)
• Leveraged cloud and GPU computing technologies for automated machine learning and analytics pipelines, such as AWS, GCP
• Worked on confluence and Jira
• Designed and implemented configurable data delivery pipeline for scheduled updates to customer facing data stores built with Python
• Proficient in Machine Learning techniques (Decision Trees, Linear/Logistic Regressors) and Statistical Modeling
• Compiled data from various sources to perform complex analysis for actionable results
• Measured Efficiency of Hadoop/Hive environment ensuring SLA is met
• Optimized the Tensorflow Model for efficiency
• Analyzed the system for new enhancements/functionalities and perform Impact analysis of the application for implementing ETL changes
• Implemented a Continuous Delivery pipeline with Docker, and Git Hub and AWS
• Built performant, scalable ETL processes to load, cleanse and validate data
• Participated in the full software development lifecycle with requirements, solution design, development, QA implementation, and product support using Scrum and other Agile methodologies
• Collaborate with team members and stakeholders in design and development of data environment
• Preparing associated documentation for specifications, requirements, and testing

Environment: AWS, Gcp, Bigquery, Gcs Bucket, G-Cloud Function, Apache Beam, Cloud Dataflow, Cloud Shell, Gsutil, Bq Command Line Utilities, Dataproc, Cloud Sql, Mysql, Posgres, Sql Server, Python, Scala, Spark, Hive, Spark - Sql

Data Engineer

Confidential, Boise, ID

Responsibilities:

• Created and executed Hadoop Ecosystem installation and document configuration scripts on Google Cloud Platform.
• Transformed batch data from several tables containing tens of thousands of records from SQL Server, MySQL, PostgreSQL, and CSV file datasets into data frames using PySpark.
• Researched and downloaded jars for Spark-Avro programming.
• Developed a PySpark program that writes data frames to HDFS as Avro files.
• Utilized Spark's parallel processing capabilities to ingest data.
• Created and executed HQL scripts that create external tables in a raw layer database in Hive.
• Developed a Script that copies avro formatted data from HDFS to External tables in raw layer.
• Created PySpark code that uses Spark SQL to generate dataframes from avro formatted raw layer and writes them to data service layer internal tables as orc format.
• In charge of PySpark code, creating data frames from tables in data service layer and writing them to a Hive data warehouse.
• Installed Airflow and created a database in PostgreSQL to store metadata from Airflow.
• Configured documents which allow Airflow to communicate to its PostgreSQL database.
• Developed Airflow DAGs in python by importing the Airflow libraries.
• Utilized Airflow to schedule automatically trigger and execute data ingestion pipeline.

Spark Developer

Confidential

Responsibilities:

• Imported required modules such as Keras and NumPy on Spark session, also created directories for data and output.
• Read train and test data into the data directory as well as into Spark variables for easy access and proceeded to train the data based on a sample submission.
• The images upon being displayed are represented as NumPy arrays, for easier data manipulation all the images are stored as NumPy arrays.
• Created a validation set using Keras2DML in order to test whether the trained model was working as intended or not.
• Defined multiple helper functions that are used while running the neural network in session. Also defined placeholders and number of neurons in each layer.
• Created neural networks computational graph after defining weights and biases.
• Created a TensorFlow session which is used to run the neural network as well as validate the accuracy of the model on the validation set.
• After executing the program and achieving acceptable validation accuracy a submission was created that is stored in the submission directory.
• Executed multiple SparkSQL queries after forming the Database to gather specific data corresponding to an image.

Environment: Scala, Python, PySpark, Spark, Spark ML Lib, Spark SQL, TensorFlow, NumPy, Keras, PowerBI.

Data Engineer

Confidential

Responsibilities:

• Migrating data from FS to Snowflake within the organization
• Imported Legacy data from SQL Server and Teradata into Amazon S3.
• Created consumption views on top of metrics to reduce the running time for complex queries.
• Exported Data into Snowflake by creating Staging Tables to load Data of different files from Amazon S3.
• Compare the data in a leaf level process from various databases when data transformation or data loading takes place.
• I need to analyze and look into the data quality when these types of loads are done (To look for any data loss, data corruption).
• As a part of Data Migration, wrote many SQL Scripts for Mismatch of data and worked on loading the history data from Teradata SQL to snowflake.
• Developed SQL scripts to Upload, Retrieve, Manipulate and handle sensitive data (National Provider Identifier Data I.e. Name, Address, SSN, Phone No) in Teradata, SQL Server Management Studio and Snowflake Databases for the Project
• Worked on to retrieve the data from FS to S3 using spark commands
• Built S3 buckets and managed policies for S3 buckets and used S3 bucket and Glacier for storage and backup on AWS
• Created Metric tables, End user views in Snowflake to feed data for Tableau refresh.
• Generated Custom SQL to verify the dependency for the daily, Weekly, Monthly jobs.
• Using Nebula Metadata, registered Business and Technical Datasets for corresponding SQL scripts
• Experienced in working with spark ecosystem using Spark SQL and Scala queries on different formats like text file, CSV file.
• Developed spark code and spark-SQL/streaming for faster testing and processing of data.
• Closely involved in scheduling Daily, Monthly jobs with Precondition/Post condition based on the requirement.
• Monitor the Daily, Weekly, Monthly jobs and provide support in case of failures/issues.

Environment: Snowflake, AWS S3, GitHub, Service Now, HP Service Manager, EMR, Nebula, Teradata, SQL Server, Apache Spark, Sqoop