SUMMARY

• 8+ years of extensive experience in Information Technology wif expertise on Data Analytics, Data Architect, Design, Development, Implementation, Testing and Deployment of Software Applications in Banking, Finance, Insurance, Retail and Telecom domains.
• Working experience on designing and implementation complete end to end Hadoop infrastructure using HDFS, MapReduce, Hive, HBase, Kafka, Sqoop, Spark, zookeeper, Ambari, Scala, Oozie, Yarn, No SQL, Postman and Python
• Created Data Frames and performed analysis using Spark SQL.
• Acute noledge on Spark Streaming and Spark Machine Learning Libraries.
• Hands on expertise in writing different RDD (Resilient Distributed Datasets) transformations and actions using Scala, Python and Java.
• Excellent understanding of Spark Architecture and framework, Spark Context, APIs, RDDs, Spark SQL, Data frames, Streaming, MLlib.
• Worked in agile projects delivering end to end continuous integration/continuous delivery pipeline by Integration of tools like Jenkins and AWS for VM provisioning.
• Experienced in writing teh automatic scripts for monitoring teh file systems, key MapR services.
• Implemented continuous integration & deployment (CICD) through Jenkins for Hadoop jobs.
• Good Knowledge on Cloudera distributions and in Amazon simple storage service (Amazon S3), AWS Redshift, Lambda and Amazon EC2, Amazon EMR.
• Excellent understanding of Hadoop Architecture and good Exposure in Hadoop components like Hadoop Map Reduce, HDFS, HBase, Hive, Sqoop, Cassandra, Kafka and Amazon Web services (AWS) API test, document and monitor by Postman which is easily integrate teh tests into you're build automation.
• Used Sqoop to Import data from Relational Database (RDBMS) into HDFS and Hive, storing using different formats like Text, Avro, Parquet, Sequence File, ORC File along wif compression codes like Snappy and GZip.
• Performed transformations on teh imported data and Exported back to RDBMS.
• Worked on Amazon Web service (AWS) to integrate EMR wif Spark 2 and S3 storage and Snowflake.
• Experience in writing queries in HQL (Hive Query Language), to perform data analysis.
• Created Hive External and Managed Tables.
• Implemented Partitioning and Bucketing on Hive tables for Hive Query Optimization.
• Used Apache Flume to ingest data from different sources to sinks like Avro, HDFS.
• Implemented custom interceptors for flume to filter data and defined channel selectors to multiplex teh data into different sinks.
• Excellent noledge on Kafka Architecture.
• Integrated Flume wif Kafka, using Flume both as a producer and consumer (concept of FLAFKA).
• Used Kafka for activity tracking and Log aggregation.
• Experienced in writing Oozie workflows and coordinator jobs to schedule sequential Hadoop jobs.
• Experience working wif Text, Sequence files, XML, Parquet, JSON, ORC, AVRO file formats and Click Stream log files.
• Familiar in data architecture including data ingestion pipeline design, Hadoop architecture, data modeling and data mining and advanced data processing.

PROFESSIONAL EXPERIENCE

Senior AWS Data Engineer

Confidential, Bentonville, AR

Responsibilities:

• Implemented Installation and configuration of multi - node cluster on Cloud using Amazon Web Services (AWS) on EC2.
• Handled AWS Management Tools as Cloud watch and Cloud Trail.
• Stored teh log files in AWS S3. Used versioning in S3 buckets where teh highly sensitive information is stored.
• Integrated AWS Dynamo DB using AWS lambda to store teh values of items and backup teh DynamoDB streams
• Automated Regular AWS tasks like snapshots creation using Python scripts.
• Designed data warehouses on platforms such as AWS Redshift, Azure SQL Data Warehouse, and other high-performance platforms.
• Install and configure Apache Airflow for AWS S3 bucket and created dags to run teh Airflow
• Prepared scripts to automate teh ingestion process using Pyspark and Scala as needed through various sources such as API, AWS S3, Teradata and Redshift.
• Created multiple scripts to automate ETL/ ELT process using Pyspark from multiple sources
• Developed Pyspark scripts utilizing SQL and RDD in spark for data analysis and storing back into S3
• Developed Pyspark code to load from stg to hub implementing teh business logic.
• Developed code in Spark SQL for implementing Business logic wif python as programming language.
• Designed, Developed and Delivered teh jobs and transformations over teh data to enrich teh data and progressively elevate for consuming in teh Pub layer of teh data lake.
• Worked on Sequence files, Map side joins, bucketing, partitioning for hive performance enhancement and storage improvement.
• Wrote, compiled, and executed programs as necessary using Apache Spark in Scala to perform ETL jobs wif ingested data.
• Used Spark Streaming to divide streaming data into batches as an input to Spark engine for batch processing.
• Maintained Kubernetes patches and upgrades.
• Managed multiple Kubernetes clusters in a production environment.
• Wrote Spark applications for data validation, cleansing, transformation, and custom aggregation and used Spark engine, Spark SQL for data analysis and provided to teh data scientists for further analysis
• Developed various UDFs in Map-Reduce and Python for Pig and Hive.
• Data Integrity checks has been handled using hive queries, Hadoop, and Spark.
• Worked on performing transformations & actions on RDDs and Spark Streaming data wif Scala.
• Implemented teh Machine learning algorithms using Spark wif Python.
• Profile structured, unstructured, and semi-structured data across various sources to identify patterns in data and Implement data quality metrics using necessary query's or python scripts based on source.
• Designs and implementing Scala programs using Spark Data frames and RDDs for transformations and actions on input data.
• Improved teh Hive queries performance by implementing partitioning and clustering and Optimized file formats (ORC).

Environment: AWS, JMeter, Kafka, Ansible, Jenkins, Docker, Maven, Linux, Red Hat, GIT, Cloud Watch, Python, Shell Scripting, Golang, Web Sphere, Splunk, Tomcat, Soap UI, Kubernetes, Terraform, PowerShell.

Data Engineer

Confidential, New York, NY

Responsibilities:

• Worked on AWS Data pipeline to configure data loads from S3 to into Redshift.
• Using AWS Redshift, me 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
• me has written shell script to trigger data Stage jobs.
• Assist service developers in finding relevant content in teh existing reference 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 teh raw data by using Hashing algorithms concepts on client specified columns.
• Responsible for Design, Development, and testing of teh database and Developed Stored Procedures, Views, and Triggers
• Created Tableau reports wif complex calculations and worked on Ad-hoc reporting using PowerBI.
• Creating DataModel data correlates all teh metrics and gives a valuable output.
• Worked on teh tuning of SQL Queries to bring down run time by working on Indexes and Execution Plan.
• Exploring wif Spark to improve teh performance and optimization of teh existing algorithms in Hadoop using Spark context, Spark-SQL, postgreSQL, Data Frame, OpenShift, Talend, pair RDD's
• Involved in integration of Hadoop cluster wif spark engine to perform BATCH and GRAPHX operations.
• Performed data preprocessing and feature engineering for further predictive analytics using Python Pandas.
• Generated report on predictive analytics using Python and Tableau including visualizing model performance and prediction results.
• 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.
• 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, 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 halped manage teh data conversion migration from teh legacy system to teh target snowflake database.
• Design, develop, and test dimensional data models using Star and Snowflakes chema methodologies under teh Kimball method.
• Implement ad-hoc analysis solutions using Azure Data Lake Analytics/Store, HDInsight
• Developed data pipeline using Spark, Hive, Pig, python, Impala, and HBase to ingest customer
• Involved in converting Hive/SQL queries into Spark transformations using Spark RDDs, Python and Scala.
• Worked on a direct query using PowerBI to compare legacy data wif teh current data and generated reports and stored and dashboards.
• Designed SSIS Packages to extract, transfer, load (ETL) existing data into SQL Server from different environments for teh SSAS cubes (OLAP) SQL Server reporting services (SSRS). Created & formatted Cross-Tab, Conditional, Drill-down, Top N, Summary, Form, OLAP, Subreports, ad-hoc reports, parameterized reports, interactive reports & custom reports
• Created action filters, parameters and calculated sets for preparing dashboards and worksheets using PowerBI.
• Developed visualizations and dashboards using PowerBI
• Sticking to ANSI SQL language specification wherever possible, and providing context about similar functionality in other industry-standard engines (e.g. referencing PostgreSQL function documentation)
• Used ETL to implement teh Slowly Changing Transformation, to maintain Historically Data in Data warehouse.
• Performing ETL testing activities like running teh Jobs, extracting teh data using necessary queries from database transform, and upload into teh Data warehouse servers.
• Created dashboards for analyzing POS data using Power BI.

Environment: MS SQL Server 2016, T-SQL, SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS), SQL Server Analysis Services (SSAS), Management Studio (SSMS), Advance Excel (creating formulas, pivot tables, Hlookup, Vlookup, Macros), Spark, Python, ETL, Power BI, Tableau, Presto, Hive/Hadoop, Snowflakes, Power BI, AWS Data Pipeline, IBM Cognos 10.1, Data Stage, Cognos

Sr. AWS Data Engineer

Confidential, NY

Responsibilities:

• Processed teh Web server logs by developing Multi-hop flume agents by using Avro Sink and loaded into MongoDB for further analysis, also extracted files from MongoDB through Flume and processed.
• Expert noledge on MongoDB, NoSQL data modeling, tuning, disaster recovery backup used it for distributed storage and processing using CRUD.
• Extracted and restructured teh data into MongoDB using import and export command line utility tool.
• Experience in setting up Fan-out workflow in flume to design v shaped architecture to take data from many sources and ingest into single sink.
• Experience in creating tables, dropping, and altered at run time wifout blocking updates and queries using HBase and Hive.
• Experience in working wif different join patterns and implemented both Map and Reduce Side Joins.
• Wrote Flume configuration files for importing streaming log data into HBase wif Flume.
• Imported several transactional logs from web servers wif Flume to ingest teh data into HDFS.
• Using Flume and Spool directory for loading teh data from local system (LFS) to HDFS.
• Installed and configured pig, written Pig Latin scripts to convert teh data from Text file to Avro format.
• Created Partitioned Hive tables and worked on them using HiveQL.
• Loading Data into HBase using Bulk Load and Non-bulk load.
• Worked on continuous Integration tools Jenkins and automated jar files at end of day.
• Worked wif Tableau and Integrated Hive, Tableau Desktop reports and published to Tableau Server.
• Developed MapReduce programs in Java for parsing teh raw data and populating staging Tables.
• Experience in setting up teh whole app stack, setup, and debug log stash to send Apache logs to AWSElastic search.
• Developed Spark code using Scala and Spark-SQL/Streaming for faster testing and processing of data.
• Analyzed teh SQL scripts and designed teh solution to implement using Scala.
• Used Spark-SQL to Load JSON data and create Schema R DD and loaded it into Hive Tables and handled structured data using Spark SQL.
• Implemented Spark Scripts using Scala, Spark SQL to access hive tables into Spark for faster processing of data.
• 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 teh data in In Azure Databricks.
• Tested Apache Tez for building high performance batch and interactive data processing applications on Pig and Hive jobs.
• Exploring wif Spark to improve teh performance and optimization of teh existing algorithms in Hadoop using Spark context, Spark-SQL, postgreSQL, Scala, Data Frame, Impala, OpenShift, Talend, pair RDD's.
• Setup data pipeline using in TDCH, Talend, Sqoop and PySpark on teh basis on size of data loads
• Implemented Real time analytics on Cassandra data using thrift API.
• Designed Columnar families in Cassandra and Ingested data from RDBMS, performed transformations and exported teh data to Cassandra.
• Leading teh testing efforts in support of projects/programs across a large landscape of technologies (Unix, Angular JS, AWS, Sause LABS, Cucumber JVM, Mongo DB, GITHub, BitBucket, SQL, NoSQL database, API, Java, Jenkins

Environment: Hadoop (HDFS, MapReduce), Databricks, Spark, Talend, Impala, Hive, postgreSQL, Jenkins, NiFi, Scala, Mongo DB, Cassandra, Python, Pig, Sqoop, Hibernate, spring, Oozie, AWS Services EC2, S3, Autoscaling, Scala, Azure, Elastic Search, DynamoDB, UNIX Shell Scripting, TEZ.

Hadoop Engineer/Data Engineer

Confidential

Responsibilities:

• Involved in complete Implementation lifecycle, specialized in writing custom MapReduce, and Hive
• Extensively used Hive/HQL or Hive queries to query or search for a string in Hive tables in HDFS
• Continuous monitoring and managing teh Hadoop cluster using Cloudera Manager
• Implemented Spark using Python and Spark SQL for faster processing of data
• Used Spark for interactive queries, processing of streaming data and integration wif popular NoSQL database
• Used teh Spark -Cassandra Connector to load data to and from Cassandra
• Implemented test scripts to support test driven development and continuous integration.
• Dumped teh data from HDFS to Oracle database and vice-versa using Sqoop
• Extensively involved in Installation and configuration of Cloudera Hadoop Distribution.
• Provided support for EBS, Trusted Advisor, Cloud Watch, Cloud Front, IAM, Security Groups, Auto-Scaling, AWS CLI and Cloud Watch Monitoring creation and update.
• Worked wif Amazon Web Services (AWS) using EC2 for computing and S3 as storage mechanism
• Deployed Lambda and other dependencies into AWS to automate EMR Spin for Data Lake jobs
• Scheduled spark applications/Steps in AWS EMR cluster.
• Extensively used event-driven and scheduled AWS Lambda functions to trigger various AWS resources.
• Implemented advanced procedures like text analytics and processing using teh in-memory computing capabilities like Apache Spark written in Scala.
• Developed spark applications for performing large scale transformations and denormalization of relational datasets.
• Developed and executed a migration strategy to move Data Warehouse from SAP to AWS Redshift.
• Loaded data into teh cluster from dynamically generated files using Flume and from relational database management systems using Sqoop.
• Used Spark Streaming to divide streaming data into batches as an input to spark engine for batch processing.
• Worked on analyzing Hadoop cluster and different Big Data analytic tools including Pig, hive, HBase, Spark and Sqoop.
• Exported data from HDFS to RDBMS via Sqoop for Business Intelligence, visualization, and user report generation.
• Loading teh data from multiple Data sources like (SQL, DB2, and Oracle) into HDFS using Sqoop and load into Hive tables.
• Performed Real time event processing of data from multiple servers in teh organization using Apache Storm by integrating wif apache Kafka.
• Performed Impact Analysis of teh changes done to teh existing mappings and provided teh feedback
• Create mappings using reusable components like worklets, mapplets using other reusable transformations.
• Participated in providing teh project estimates for development team efforts for teh offshore as well as on-site.
• Coordinated and monitored teh project progress to ensure teh timely flow and complete delivery of teh project
• Worked on Informatica Source Analyzer, Mapping Designer & Mapplet, and Transformations.

Environment: Hadoop, HDFS, Hive, MapReduce, Impala, Sqoop, SQL, Informatica, Python, Flume, PySpark, Yarn, Pig, Oozie, Linux, AWS, Tableau, Maven, Jenkins, Cloudera, SAS (BI & DI), PL/SQL, Autosys, Oracle, Sql Server,No Sql, TeraData.

ETL Developer

Confidential

Responsibilities:

• Worked on Data Profiling, Data Cleansing and Data Mining.
• Modified the logical and physical data models for the new feeds.
• Developed BTEQ scripts to load the data from the staging tables to the base tables.
• Performance tuning of long running scripts. Stats/ index recommendations.
• Performed developer DBA tasks like creating users/roles/profiles and space allocation tasks.
• Worked on Teradata Manager to monitor and manage resource utilization.
• Capacity planning for new applications.
• Production system monitoring and providing support for any batch failures.

Environment: Teradata V12 on UNIX MP-RAS, Informatica 7.1, BTEQ, FASTLOAD, MULTILOAD, FASTEXPORT, UNIX Shell Scripting, Teradata Manager/PMON.