SummaryOver 5 years of engineering and finance experience as a Data Developer with cross platform integration experience using Hadoop and Spark architecture Hands-on experience configuring as well as installing Hadoop Ecosystem - HDFS, MapReduce, Pig, Hive, Oozie, Flume, HBase, Spark, Sqoop, Flume and Oozie. Strong understanding of various Hadoop services, MapReduce and YARN architecture. Vast knowledge in importing as well as exporting data in HDFS using SQOOP. Automated transfer of data from Hbase by developing Map Reduce jobs Expertise in analysis using PIG, HIVE and MapReduce. Experience in HDFS data storage and support for running map-reduce jobs. Involved in Infrastructure set up and installation of HDP stack on Amazon Cloud. Experienced with ingesting data from RDBMS such as SQL Teradata into HDFS using Sqoop and Oracle. Expertise in Hadoop architecture, HDFS, Map-reduce, Oozie, Sqoop, Spark, Hive, Zookeeper and NoSQL databases. Deployed and configured clusters in Cloudera Manager. Set up backups and disaster recovery for Data Node and Name Node metadata as well as sensitive data on clusters. Expertise in im plementing and designing HDFS access controls, directory and file permissions user authorization that facilitates stable, secure access for multiple users in a large multi-tenant cluster Knowledge on exporting as well as importing stream data into HDFS using Flume, Spark, and Kafka messaging systems. Utilized various schedulers on the Job tracker to share resources within clusters for Map Reduce jobs such as FIFO scheduler, Fair Scheduler and Capacity Scheduler. Monitored jobs with YARN, and provisioned, configured, and installed HBase, Kafka, Hive, Oozie, Sqoop, Ranger, Storm, Flume, Spark as well as maintained total architecture. AWS services for cloud migration such as S3, Redshift, EMR, Glue, Athena and DynamoDB. Expertise in Vertica DB architecture High Availability and column orientation. Great knowledge of Agile and Scrum methodologies, Behavior Driven Development, Domain Driven Design,Test Driven Development and continuous integration as well as delivery. Skilled in defining functional and gathering user interface requirements for applications and websites. Expertise in real time analysis by utilizing RDD, Datasets, Data Frames and Streaming API in Apache Spark. Expertise in using Spark Resilient distributed datasets and dataframe APIs over the Cloudera platform to perform analytics on Hive data by integrating Hadoop with Kafka. Expertise in uploading Click stream data from Kafka to HDFS. Expert in utilizing Kafka for messaging and publishing subscriber based messaging systems . Worked with NoSQL databases such as Cassandra, HBASE, PostgreSQL, MongoDB, Redis, DynamoDB SkillsSQL transactional replicationsSAN technologiesReverse engineering skillsWarehouse modelsSecurity ProtocolsEnterprise information architectureQuality analysisDeep learningData miningData analyticsCritical thinking ExperienceData Engineer,07 2021toCurrentPrincipal Financial Group Pittsburgh,PA, Analyze, design and build Modern data solutions using Azure PaaS service to support visualization of data. Understand current Production state of application and determine the impact of new implementation on existing business processes. 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. Created Pipelines in ADF using LinkedServices Datasets Pipeline to Extract, Transform and load data from different sources like Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool and backwards. Developed Spark applications using Pyspark andSpark-SQL for data extraction, transformation andaggregation from multiple file formats for analyzing transforming the data to uncover insights into thecustomer usage patterns. Responsible for estimating the cluster size, monitoringand troubleshooting of the Spark databricks cluster. Experienced in performance tuning of SparkApplications for setting right Batch Interval time,correct level of Parallelism and memory tuning.Adept in troubleshooting and identifying current issues and providing effective solutions.Managed performance monitoring and tuning while identifying and repairing issues within database realm.Contributed to maintaining Type databases in conjunction with data development and software engineering teams.Assisted solution providers with definition and implementation of technical and business strategies.Collaborated with solution architects to define database and analytics engagement strategies for operational territories and key accounts.Data Engineer,07 2020to06 2022Principal Financial Group Portland,OR,Worked on MongoDB by using CRUD (Create, Read, Update and Delete), Indexing, Replication, and Sharding featuresInvolved in designing the row key in HBase to store Text and JSON as key values in the HBase table and designed row key in such a way to get scan it in sorted orderOozie was integrated with the Hadoop stack which consists of Map-Reduce, Hive, Sqoop, and Pig and Unix shell scriptsWhile working on Hive tables used Hive QL, designed and Implemented Partitioning(Static, Dynamic) Buckets on HivePerformed Cache and Persist as well as Checkpointing when utilizing Spark Streaming APIs to build common learning data models to get near real-time data from Kafka and persisted into CassandraConducted cluster coordination services with Zookeeper and monitored the workload, capacity planning and job performance through Cloudera ManagerBuilt applications by utilizing Maven and integrated with Continuous Integration servers such as Jenkins to build jobsDeployed, maintained, and configured Test and multi-node Dev Kafka Clusters as well as handled clusters and implemented data ingestion for real time processing in KafkaCreated cubes in Talend for various aggregation types of data from PostgreSQL and MS SQL server to visualize dataMonitored Name Node health status in Hadoop as well as the number of Data Nodes and Task trackers running along with automating jobs to pull data from various MySQL data sources to push result set data to HDFSCreated story telling dashboards through Tableau Desktop to publish on Tableau Server and integrated GitHub for version control tools to maintain the versions in projectsDeployed Spark applications in python and utilized Datasets and DataFrames in Spark SQL for processing data fasterLoaded transactional data with Sqoop from Teradata, created managed and external tables in Hive, and worked with semi-structured and structured data of 5 Petabytes in sizeConstructed MapReduce jobs to validate, clean, and access data and worked with Sqoop jobs with incremental load to populate and load into Hive External tablesDesigned strategies to optimize distribution of weblog data over clusters, in addition to exporting and importing stored web log data into Hive and HDFS through SqoopResponsibilities of building scalable data solutions that are distributed through Hadoop and Cloudera as well as developed and designed automated test scripts in PythonIntegrated Apache Storm with Kafka to perform web analytics and to perform clickstream data from Kafka to HDFSDeveloped SQL scripts and designed solutions to implement Spark with Hive Generic UDFs for incorporating business logic within Hive queriesDeveloped data pipelines in AWS using S3, EMR, Redshift to extract data from weblogs to store into HDFSTransmitted streaming data from Kafka to HBase, Hive, and HDFS by integrating Apache Storm and wrote Pig scripts for transforming raw data from various data sources to form baseline dataParticipated in Agile meetings Ford Credit Customer Data domain, conducted daily scrum meetings and spring planningExpanded and optimized data pipelines and architecture as well as optimized data flow and collectionCreated pipelines from scratch using PySpark and scheduled jobs using AirflowStream processed data in Kafka streaming wrote Producer, Consumer, Connector and Streams API to handle stream of records, subscription of topics, consume input, and build reusable producers and consumersWorked with unstructured datasets such as IoT sources, sensor data, XML and JSON document sourcesWorked with on prem clusters as well as clusters on the cloud and used GCP Big Query, Data Fusion, and DataFlowScaled up architecture with Google Kubernetes and set up load balancingWorked on various optimization techniques in Spark such as cache and persist, using accumulators , bucketing and partitioning, garbage collection tuning, data serialization, windowing functions, and broadcast variablesUsed numerous Spark transformations such as groupByKey() reduceByKey(), flatMap(), filter(), sample(), union(), etcUtilized Dataproc to spin up clusters, Dataprep for data analysis, and Dataflow for streaming data using Apache BeamDealt with VPC controls on Google Cloud Platform and CMEK encryption for data securityEnvironment Hadoop, HDFS, HBase, Spark(Python and Scala), Azure, Databricks, Scala, Hive, Kafka, MapReduce, Sqoop, ETL, Java, Python, PostgreSQL, SQL Server, Teradata, Unix Linux.Big Data Developer,05 2017to05 2020Cognizant Technology Solutions Mount Laurel,NJ,Performed query tuning in HiveQL as well as performance tuning transformations in Pyspark using Spark RDDs and PythonUsed lambda functions to create a Serverless Data intake pipeline on AWSUsing python, constructed a Spark Streaming pipeline to receive real-time data from Apache Kafka and store it in DynamoDBImplemented Apache Spark data processing module to handle data from multiple RDBMS and Streaming sources, then compiled Apache Spark applications using Scala and PythonExtensive experience designing and scheduling multiple Spark Streaming batch Jobs in Python (pyspark) and ScalaAchieved high-throughput, scalable, fault-tolerant stream processing of live data streams using Apache Spark StreamingInvolved with the use for creating and saving data frames using various Python modules with pysparkSqooped data and performed Hive queries for data ingestion from relational databases to analyze historical dataExperienced with Elastic MapReduce (EMR) as well as setting up environments on amazon AWS EC2 instances for pipelines in AWSExpertise in handling Hive queries using Spark SQL such as window functions and aggregationsRan Spark applications on Docker using EMR and used AWS Glue data catalog as the metastore in Spark SQLConfigured different File Formats like Avro, parquet for HIVE querying and processing based on business logicUtilized Sequence files, RC files, Map side joins, bucketing, partitioning for Hive performance enhancement and storage improvementImplemented Hive UDF to implement business logic and performed extensive data validation using HiveInvolved in loading the structured and semi structured data into spark clusters using Spark SQL and Data Frames APIUtilized AWS CloudWatch to monitor the performance environment instances for operational and performance metrics during load testingScripting Hadoop package installation and configuration to support fully automated deploymentsInvolved in chef-infra maintenance including backup security fix on Chef ServerDeployed application updates using JenkinsInstalled, configured, and managed JenkinsTriggering the SIT environment build of the client remotely through JenkinsDeployed and configured Git repositories with branching, forks, tagging, and notificationsWorked on MongoDB database concepts such as locking, transactions, indexes, Shading, replication, schema designViewing the selected issues of web interface using SonarQubeDeveloped a fully functional login page for the company s user facing website with complete UI and validationsInstalled, Configured and utilized AppDynamics (Tremendous Performance Management Tool) in the whole JBoss Environment (Prod and Non-Prod)Installed and installed Hive in a Hadoop cluster and assisted business users application teams in fine-tuning their HIVE QL for optimal performance and efficient use of cluster resourcesUtilized Oozie workflow for ETL Process for critical data feeds across the platformConfigured Ethernet bonding for all Nodes to double the network bandwidthConfigured Kerberos Security Authentication protocol for existing clustersConstructed the use of Zookeeper failover controller (ZKFC) and Quorum Journal nodes for high availability for significant production clusters and automatic failover controller createdInstallation and deployment of many Apache Hadoop nodes on an AWS EC2 system, as well as development of Pig Latin scripts to replace the old traditional process with Hadoop, and data feeding to AWS S3Experience with AWS CloudFront, including the creation and management of distributions that provide access to an S3 bucket or an HTTP server running on EC2 instancesDeveloped Python scripts, UDFs using both Data frames SQL and RDD MapReduce in Spark 1.6 for Data Aggregation, queries and writing data back into OLTP system through Sqoop And Developed enterprise application using PythonConstructed Spark application performance optimization, including determining the appropriate Batch Interval time, Parallelism Level, and Memory TuningExperience and hands-on knowledge in Akka and LIFT FrameworkUsed PostgreSQL and No-SQL database and integrated with Hadoop to develop datasets on HDFSEnvironment HDFS, Map Reduce, Hive 1.1.0, Kafka, Hue 3.9.0, Pig, Flume, Oozie, Sqoop, Apache Hadoop 2.6, Spark, SOLR, Storm, Cloudera Manager, Red Hat, MySQL, Prometheus, Docker, Puppet, YARN,, Spark-SQL, Python, Amazon AWS, Elastic Search, Tableau, LinuxKey Skills SQL, Apache hive, Apache Spark(Databricks, Jupyter Notebook), Anaconda, Python (Django, Pandas, Flask, Keras, NumPy, Scikit-learn, MatPlotLib, Tensorflow, ), Time Series Forecasting, A B testing, Bayesian methods, PowerBI, Microsoft(Word, Excel, Powerpoint), Java, Data Visualization, Analytical Skills, Cost Accounting, Corporate Finance Knowledge, Statistical Analysis(RStudio) Education and TrainingBachelor s Computer Science,Expected intoUttara Institute of Business and Technology-,GPA  SEP  The Senior Manager, Data Architecture Data Engineering is responsible for translating business strategic goals into future-state data engineering and data science solutions. This includes delivery full life-cycle from execution roadmaps, to implementation, and to production operations. This role drives the creation and maintenance of enterprise data and analytics capabilities, data reference architectures, and integrated data analytics technology roadmaps that will guide our strategic investments and enterprise strategy.Translate business strategic goals into actionable future-state data engineering and data science architectures and solutions.Delivery of full life-cycle Data Solutions from execution roadmaps, to implementation, and to operations. This includes a specific focus on ELT and ETL Data Pipeline solutions from inception to production.Lead end to end solution delivery of enterprise data warehouse, data marts, and data lake with governance.Drive the creation and maintenance of enterprise data and analytics capabilities, data solutions, and integrated data analytics technology roadmaps that will guide our strategic investments and enterprise strategy.Deliver architecture connecting Data Science, Data Engineering, and Data Analytics practices together to deliver impactful data products.Strong partnership with DevOps, DataOps, business partners, engineering teams, and data management.A minimum of ten (10) years of experience in IT with a minimum (6) years in data architecture and data solutions are required.Successful track-record in managing a team of Data Engineers and Data ScientistsSkilled in hybrid cloud data platforms and analytical technologies including Azure andor GCP data infrastructure, Spark, Data Lake, Data Warehouse and Data Marts, BI reporting with PowerBI, Metadata Management, Data Governance, Master Data Management, Data Catalog, and Business Glossary.Ability to document, communicate, implement, and operationalize complex architectures.Experience with ELT and ETL Data Pipeline solutions from inception to production.Proficient in SQL, NoSQL and columnar databases.Experience with SparkPyspark and JSON.Excellent grasp of Computer Science fundamentals - data structures and algorithms.Must have Panda Data Frames knowledge and best practices, execution engines, DAGs (airflow, dagster, luigi, etc), and Kubernetes.Proficient with GitLabFamiliarity with Azure data factory preferred, Google data flow or AWS GlueExperience with Biomedical Informatics, S3Azure blob store, Machine Learning and AI, Unstructured Data, and Data Democratization is preferred.Experience with Agile methodologies, Kanban preferred, and using tools like Jira and Confluence to facilitate the work.Bachelor s degree in computer science or a related discipline required. Master s degree preferred. RECRUITERS NOTES The role will definitely be hands-on and will require excellent communication skills. The hiring manager mentioned the following technologies AImachine learning data warehouses product support Cloud experienceAzure must be open to new technologies. Working with the Product Development Team. Act as the point of contact will create a group that helps the product teams. Create data warehouses for analytics. MUST have excellent communication skills verbal and written. This person will envision strategy and must be able to effectively communicate that strategy to others. The organization has asked me not to advertise the compensation. If you contact me I would be more than willing to share it with you. For over 50 years this non-profit organization has made it their mission to protect patients from unsafe and ineffective medical technologies and practices. More than 5,000 healthcare institutions and systems worldwide including four out of every five U.S. hospitals rely on the services of this organization to guide their operational and strategic decisions. In addition, they serve public and private payers, federal and state agencies, policymakers, ministries of health, associations, and accrediting agencies. Benefit from a healthy work-life balance while staying on the leading edge of technology and thriving in an innovative startup-like culture minus the risk. Sleep well knowing you are helping achieve a world where safe, high-quality healthcare is accessible to everyone. Excellent benefits to include work from home, flex-time and 19 days starting vacation. Its a nice place to work!

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