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.
• Experience in change implementation, monitoring and troubleshooting of AWS Snowflake databases and cluster related issues.
• 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.
• Working on Big Data infrastructure for batch processing as well as real - time processing. Responsible for building scalable distributed data solutions using the Hadoop eco system.
• Experience in cloud data migration using AWS and Snowflake.
• Excellent understanding of Hadoop ArchitCaecture 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 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 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. Experience optimizing ETL workflows.
• Good Exposure in Data Quality, Data Mapping, Data Filtration using Data warehouse ETL tools like Talend, Informatica, DataStage, Ab - initio
• Good Exposure to create various dashboard in Reporting Tools like SAS, Tableau, Power BI, BO, QlikView used various filters, sets while dealing wif huge volume of data.
• Recreated existing SQL Server objects in snowflake.
• Experience in various Database such as Oracle, Teradata, Informix and DB2.
• Experience wif NoSQL like MongoDB, HBase and PostgreSQL like Greenplum
• Worked in complete Software Development Life Cycle like Analysis, Design, Development, Testing, Implementation and Support using Agile and Waterfall Methodologies.
• Demonstrated a full understanding of teh Fact/Dimension data warehouse design model, including star and snowflake design methods.

TECHNICAL SKILLS

Big Data Ecosystem: HDFS, MapReduce, HBase, Pig, Hive, Sqoop, Kafka Flume, Cassandra, Impala, Oozie, Zookeeper, MapR, Amazon Web Services (AWS), EMR

Machine Learning: Classification Algorithms Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor (KNN), Gradient Boosting Classifier, Extreme Gradient Boosting Classifier, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayes Classifier, Extra Trees Classifier, Stochastic Gradient Descent, etc.

Cloud Technologies: AWS, Azure, Google cloud platform (GCP)

IDE’sIntelliJ: Eclipse, Spyder, Jupyter

Ensemble and Stacking: Averaged Ensembles Weighted Averaging, Base Learning, Meta Learning, Majority Voting, Stacked Ensemble, Auto ML - Scikit-Learn, ML jar, etc.

Databases: Oracle 11g/10g/9i, MySQL, DB2, MS SQL Server, HBASE

Programming: Query Languages Java, SQL, Python Programming (Pandas, NumPy, SciPy, Scikit-Learn, Seaborn, Matplotlib, NLTK), NoSQL, PySpark, PySpark SQL, SAS, R Programming (Caret, Glmnet, XGBoost, rpart, ggplot2, sqldf), RStudio, PL/SQL, Linux shell scripts, Scala.

Data Engineer: Big Data Tools / Cloud / Visualization / Other Tools Databricks, Hadoop Distributed File System (HDFS), Hive, Pig, Sqoop, MapReduce, Spring Boot, Flume, YARN, Hortonworks, Cloudera, Mahout, MLlib, Oozie, Zookeeper, etc. AWS, Azure Databricks, Azure Data Explorer, Azure HDInsight, Salesforce, GCP, Google Shell, Linux, PuTTY, Bash Shell, Unix, etc., Tableau, Power BI, SAS, We Intelligence, Crystal Reports, Dashboard Design.

PROFESSIONAL EXPERIENCE

Confidential, Bothell, WA

AWS Data Engineer

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.
• Involved in data migration to snowflake using AWS S3 buckets.
• Customer management platform, is a multi-tenant application, has capability to onboard customer enrollment data through high volume batch processing application, real time enrollment through API, outbound events through batch & API integration, customer management, administration and correspondence.
• Designed data warehouses on platforms such as AWS Redshift, 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.
• Works on loading data into Snowflake DB in the cloud from various sources
• 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.
• Ensure ETL/ELT’s succeeded and loaded data successfully in Snowflake DB.
• Created DWH, Databases, Schemas, Tables, write SQL queries against Snowflake.
• 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.
• Integrated and automated data workloads to Snowflake Warehouse.
• 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.
• Built different visualizations and reports in tableau using Snowflake data.
• 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.

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.

Confidential, New York, NY

Azure Data Engineer

Responsibilities:

• Designed, Developed, and implemented the Azure Data factory framework (V2) with Error logging to populate data in the Azure SQL Data warehouse from Azure Blob storage and Azure data lake store.
• Implemented large Lambda architectures using Azure Data platform capabilities like Azure Data Lake, Azure Data Factory, Azure Data Catalog, HDInsight, Azure SQL Server, Azure ML and Power BI.
• Experience in Developing Spark applications using Spark - SQL in Databricks for data extraction, transformation, and aggregation from multiple file formats for analysing & transforming the data to uncover insights into the customer usage patterns.
• Working experience with data streaming process with Kafka, Apache Spark, Hive.
• Developed JSON Scripts for deploying the Pipeline in Azure Data Factory (ADF) that process the data using the SQL Activity.
• Used Azure Databricks for fast, easy, and collaborative spark-based platform on Azure.
• Used Databricks to integrate easily with the whole Microsoft stack.
• 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.
• Design Data Lake storage solution for Data science Project using Azure Data factory Pipelines.
• 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 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

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, Data Pipeline, IBM Cognos 10.1, Data Stage, Cognos Report Studio 10.1, Cognos 8 & 10 BI, Cognos Connection, Cognos office Connection, Cognos 8.2/3/4, Data stage and Quality Stage 7.5

Confidential, Pataskala, Ohio

Big Data Engineer

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 without 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 AWS Elastic 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.

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.

Confidential

Data Engineer

Responsibilities:

• Gathering data and business requirements from end users and management. Designed and built data solutions to migrate existing source data in Data Warehouse to Atlas Data Lake (Big Data)
• Analyzed huge volumes of data Devised simple and complex HIVE, SQL scripts to validate Dataflow in various applications. Performed Cognos report validation. Made use of MHUB for validating Data Profiling & Data Lineage.
• Devised PL/SQL statements - Stored Procedures, Functions, Triggers, Views and packages. Made use of Indexing, Aggregation and Materialized views to optimize query performance.
• Created reports using Tableau/Power BI/Cognas to perform data validation.
• Involved in creating Created Tableau dashboards using stack bars, bar graphs, scattered plots, geographical maps, Gantt charts etc. using show me functionality. Dashboards and stories as needed using Tableau Desktop and Tableau Server
• Performed statistical analysis using SQL, Python, R Programming and Excel.
• Worked extensively wif Excel VBA Macros, Microsoft Access Forms
• Import, clean, filter and analyze data using tools such as SQL, HIVE and PIG.
• Used Python& SAS to extract, transform & load source data from transaction systems, generated reports, insights, and key conclusions.
• Developed story telling dashboards in Tableau Desktop and published them on to TableauServer which allowed end users to understand teh data on teh fly wif teh usage of quick filters for on demand needed information.
• Analyzed and recommended improvements for better data consistency and efficiency
• Designed and Developed data mapping procedures ETL-Data Extraction, Data Analysis and Loading process for integrating data using R programming.
• Effectively Communicated plans, project status, project risks and project metrics to teh project team planned test strategies in accordance wif project scope.
• Data Ingest from Sqoop & flume from Oracle data base.
• Responsible for wide-ranging data ingestion using Sqoop and HDFS commands. Accumulate ‘partitioned’ data in various storage formats like text, Json, Parquet, etc. Involved in loading data from LINUX file system to HDFS
• Storing Data Files in Google Cloud S3 Buckets daily basis. Using DataProc, Big Query to develop and maintain GCP cloud base solution.
• Start working wif AWS for storage and halding for tera byte of data for customer BI Reporting tools
• 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.
• Hands of experience in GCP, Big Query, GCS bucket, G - cloud function, cloud data flow, Pub/sub cloud shell, GSUTIL, BQ command line utilities, Data Proc, Stack driver
• Implemented Apache Airflow for authoring, scheduling and monitoring Data Pipelines
• Worked on confluence and Jira skilled in data visualization like Matplotlib and seaborn library
• Experience implementing machine learning back-end pipeline wif Pandas, Numpy

Environment: Hive, AWS, Hadoop, HDFS, Python, PL/SQL, SQL, Python, R Programming, Apache Airflow, Numpy, Pandas, Jira, PIG, Tableau, Spark, Linux, Pandas, Numpy.

Confidential

Hadoop Engineer

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

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.