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 wif 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 wif 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 wif 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 wif 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

PROFESSIONAL EXPERIENCE:

Confidential

Data Engineer

Responsibilities:

• Worked on AWS Data pipeline to configure data loads from S3 to into Redshift. Using AWS Redshift, I Extracted, transformed and loaded data from various heterogeneous data sources and destinations Created Tables, Stored Procedures, and extracted data using T - SQL for business users whenever required. Performs data analysis and design, and creates and maintains large, complex logical and physical data models, and metadata repositories using ERWIN and MB MDR I have written shell script to trigger data Stage jobs. Assist service developers in finding relevant content in the existing models. Like Access, Excel, CSV, Oracle, flat files using connectors, tasks and transformations provided by AWS Data Pipeline. Utilized Spark SQL API in PySpark to extract and load data and perform SQL queries.
• Worked on developing Pyspark script to encrypting the raw data by using Hashing algorithms concepts on client specified columns. Responsible for Design,
• Development, and testing of the database and Developed Stored Procedures, Views, and Triggers Developed Python-based API (RESTful Web Service) to track revenue and perform revenue analysis. Compiling and validating data from all departments and Presenting to Director Operation. KPI calculator Sheet and maintain dat sheet wifin SharePoint. Created Tableau reports wif complex calculations and worked on Ad-hoc reporting using PowerBI. Creating data model dat correlates all the metrics and gives a valuable output. Worked on the tuning of SQL Queries to bring down run time by working on Indexes and Execution Plan. Performing ETL testing activities like running the Jobs, Extracting the data using necessary queries from database transform, and upload into the Data warehouse servers. Pre-processing using Hive and
• Pig. Extract Transform and Load data from Sources Systems to Azure Data Storage services using a combination of Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics. Data Ingestion to one or more Azure Services - (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing the data in In Azure Databricks. Implemented Copy activity, Custom Azure Data Factory Pipeline Activities Primarily involved in Data Migration using SQL, SQL Azure, Azure Storage, and Azure Data Factory, SSIS, PowerShell. Architect & implement medium to large scale BI solutions on Azure using Azure Data Platform services (Azure Data Lake, Data Factory, Data Lake Analytics, Stream Analytics, Azure SQL DW, HDInsight/Databricks, and NoSQL DB). Migration of on-premise data (Oracle/ SQL Server/ DB2/ MongoDB) to Azure Data Lake and Stored (ADLS) using Azure Data Factory (ADF V1/V2). Developed a detailed project plan and helped manage the data conversion migration from the legacy system to the target snowflake database. Design, develop, and test dimensional data models using Star and Snowflake schema methodologies un

Confidential

Data Engineer

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 wif 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 wif Docker, and Git Hub and AWS Built performant, scalable ETL processes to load, cleanse and validate data Participated in the full software development lifecycle wif requirements, solution design, development, QA implementation, and product support using Scrum and other Agile methodologies Collaborate wif 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

Confidential

Data Engineer

Responsibilities:

• Experience in building and architecting multiple Data pipelines, end to end ETL and ELT process for Data ingestion and transformation in GCP Strong understanding of AWS components such as EC2 and S3 Implemented a Continuous Delivery pipeline wif Docker and Git Hub Worked wif g - cloud function wif Python to load Data in to Bigquery for on arrival csv files in GCS bucket Process and load bound and unbound Data from Google pub/sub topic to Bigquery using cloud Dataflow wif Python.
• Devised simple and complex SQL scripts to check and validate Dataflow in various applications. Performed Data Analysis, Data Migration, Data Cleansing, Transformation, Integration, Data Import, and Data Export through Python. Developed and deployed data pipeline in cloud such as AWS and GCP Performed data engineering functions: data extract, transformation, loading, and integration in support of enterprise data infrastructures - data warehouse, operational data stores and master data management Responsible for data services and data movement infrastructures good experience wif ETL concepts, building ETL solutions and Data modeling Architected several DAGs (Directed Acyclic Graph) for automating ETL pipelines Hands on experience on architecting the ETL transformation layers and writing spark jobs to do the processing. Gather and process raw data at scale (including writing scripts, web scraping, calling APIs, write SQL queries, writing applications)
• Experience in fact dimensional modeling (Star schema, Snowflake schema), transactional modeling and SCD (Slowly changing dimension) Devised PL/SQL Stored Procedures, Functions, Triggers, Views and packages. Made use of Indexing, Aggregation and Materialized views to optimize query performance. Developed logistic regression models (Python) to predict subscription response rate based on customers variables like past transactions, response to prior mailings, promotions, demographics, interests, and hobbies, etc. Develop near real time data pipeline using spark Process and load bound and unbound Data from Google pub/sub topic to
• Bigquery using cloud Dataflow wif Python Hands of experience in GCP, Big Query, GCS bucket, G - cloud function, cloud dataflow, Pub/suB cloud shell, GSUTIL, BQ command line utilities, Data Proc, Stack driver Implemented Apache Airflow for authoring, scheduling and monitoring Data Pipelines Proficient in Machine Learning techniques (Decision Trees, Linear/Logistic Regressors) and Statistical Modeling Worked on confluence and Jira skilled in data visualization like Matplotlib and seaborn library Hands on experience wif big data tools like Hadoop, Spark, Hive Experience implementing machine learning back-end pipeline wif Pandas, Numpy

Environment: Gcp, Bigquery, Gcs Bucket, G-Cloud Function, Apache Beam, Cloud Dataflow, Cloud Shell, Gsutil, Docker, Kubernetes, AWS, Apache Airflow, Python, Pandas, Matplotlib, seab

Confidential

Spark Developer

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 dat 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 dat 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

Confidential

SQL Developer

Responsibilities:

• Responsibilities: Gatheird business requirements and converted them into new T - SQL stored procedures in visual studio for database project. Performed unit tests on all code andpackages
• Analyzed requirement and impact by participating in Joint Application Development sessions wif business client online. Performed and automated SQL Server version upgrades, patch installs and maintained relational databases. Performed front line code reviews for other development teams. Modified and maintained SQL Server stored procedures, views, ad-hoc queries, and SSIS packages used in the search engine optimization process. Updated existing and created new reports using Microsoft
• SQL Server Reporting Services. Team consisted of 2 developers. Created files, views, tables and data sets to support Sales Operations and Analytics teams Monitored and tuned database resources and activities for SQL Server databases.