PROFESSIONAL SUMMARY:

• 8+ years of extensive experience in Information Technology with 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 knowledge 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 the automatic scripts for monitoring the 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 the tests into your 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 with compression codes like Snappy and GZip.
• Performed transformations on the imported data and Exported back to RDBMS.
• Worked on Amazon Web service (AWS) to integrate EMR with 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 in 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 the data into different sinks.
• Excellent knowledge on Kafka Architecture.
• Integrated Flume with 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 with Text, Sequence files, XML, Parquet, JSON, ORC, AVRO file formats and Clickstream log files.
• Familiar in data architecture including data ingestion pipeline design, Hadoop architecture, data modeling and data mining and advanced data processing.

PROFESSIONAL EXPERIENCE:

Sr. AWS Data Engineer

Confidential, Dallas, Texas

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 the log files in AWS S3. Used versioning in S3 buckets where the highly sensitive information is stored.
• Integrated AWS DynamoDB using AWS lambda to store the values of items and backup the 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 the Airflow
• Prepared scripts to automate the 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 the business logic.
• Developed code in Spark SQL for implementing Business logic with python as programming language.
• Designed, Developed and Delivered the jobs and transformations over the data to enrich the data and progressively elevate for consuming in the Pub layer of the 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 the data scientists for further analysis
• Developed various UDFs in Map-Reduce and Python for Pig and Hive.
• Data Integrity checks have been handled using hive queries, Hadoop, and Spark.
• Worked on performing transformations & actions on RDDs and Spark Streaming data wif Scala.
• Implemented the Machine learning algorithms using Spark with 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 the 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.

Big Data Engineer & AWS Cloud Engineer

Confidential, Mechanicsburg PA

Responsibilities:

• Worked on AWS Data pipeline to configure data loads from S3 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 a 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
• Created Tableau reports with complex calculations and worked on Ad-hoc reporting using PowerBI.
• Creating DataModel data 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.
• Exploring 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.
• 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 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 Snowflakes schema methodologies under the 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 with the current data and generated reports and stored dashboards.
• Designed SSIS Packages to extract, transfer, load (ETL) existing data into SQL Server from different environments for the 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 the Slowly Changing Transformation, to maintain Historically Data in the Data warehouse.
• Performing ETL testing activities like running the Jobs, extracting the data using necessary queries from database transform, and uploading data into the 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

Responsibilities:

• Processed the 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 knowledge on MongoDB, NoSQL data modeling, tuning, disaster recovery backup used it for distributed storage and processing using CRUD.
• Extracted and restructured the 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 a single sink.
• Experience in creating tables, dropping, and altered at runtime without blocking updates and queries using HBase and Hive.
• Experience in working with different join patterns and implementing both Map and Reduce Side Joins.
• Wrote Flume configuration files for importing streaming log data into HBase wif Flume.
• Imported several transaction logs from web servers wif Flume to ingest the data into HDFS.
• Using Flume and Spool directory for loading the data from local system (LFS) to HDFS.
• Installed and configured pig, written Pig Latin scripts to convert the 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 the end of day.
• Worked with Tableau and Integrated Hive, Tableau Desktop reports and published to Tableau Server.
• Developed MapReduce programs in Java for parsing the raw data and populating staging Tables.
• Experience in setting up the whole app stack, setup, and debug log stash to send Apache logs to AWSElastic.
• Developed Spark code using Scala and Spark-SQL/Streaming for faster testing and processing of data.
• Analyzed the SQL scripts and designed the 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 the data in In Azure Databricks.
• Tested Apache Tez for building high performance batch and interactive data processing applications on Pig and Hive jobs.
• Setup data pipeline using in TDCH, Talend, Sqoop and PySpark on the 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 export the data to Cassandra.
• Leading the testing efforts in support of projects/programs across a large landscape of technologies (Unix, Angular JS, AWS, LABS, Cucumber JVM, MongoDB, GITHub, BitBucket, SQL, NoSQL database, API, Java, Jenkins

Environment: Hadoop (HDFS, MapReduce), Databricks, Spark, Talend, Impala, Hive, postgreSQL, Jenkins, NiFi, Scala, MongoDB, 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 the 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 with popular NoSQL database
• Used the Spark -Cassandra Connector to load data to and from Cassandra
• Implemented test scripts to support test driven development and continuous integration.
• Dumped the 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, CloudFront, IAM, Security Groups, Auto-Scaling, AWS CLI and Cloud Watch Monitoring creation and update.
• Worked with 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 the 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 the 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 clusters 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 the 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 the organization using Apache Storm by integrating with apache Kafka.
• Performed Impact Analysis of the changes done to the existing mappings and provided the feedback
• Create mappings using reusable components like worklets, mapplets using other reusable transformations.
• Participated in providing the project estimates for development team efforts for the offshore as well as on-site.
• Coordinated and monitored the project progress to ensure the timely flow and complete delivery of the 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.

Confidential

ETL Developer

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.