SUMMARY

• Over 8+ years of professional experience as a Software developer in design, development, deploying and supporting large scale distributed systems.
• Around 6 years of extensive experience as a Data Engineer and Big data Developer specialized in Big Data Ecosystem - Data Ingestion, Modeling, Analysis, Integration, and Data Processing.
• Extensive experience in providing solutions for Big Data using Hadoop, Spark, HDFS, Map Reduce, YARN, Kafka, Pig, Hive, Sqoop, HBase, Oozie, Zookeeper, Cloudera Manager, Horton works.
• Strong experience working with Amazon cloud services like EMR, Redshift, DynamoDB, Lambda, Athena, Glue, S3, API Gateway, RDS, CloudWatch for efficient processing of Big Data.
• Hands on experience building PySpark, Spark Java and Scala applications for batch and stream processing involving Transformations, Actions, Spark SQL queries on RDD’s, Data frames and Datasets.
• Strong experience writing, troubleshooting and optimizing Spark scripts using Python, Scala.
• Experienced in using Kafka as a distributed publisher-subscriber messaging system.
• Strong knowledge on performance tuning of Hive queries and troubleshooting various issues related to Joins, memory exceptions in Hive.
• Exceptionally good understanding of partitioning, bucketing concepts in Hive and designed both Managed and External tables in Hive.
• Experience in importing and exporting data between HDFS and Relational Databases using Sqoop.
• Experience in real time analytics with Spark Streaming, Kafka and implementation of batch processing using Hadoop, Map Reduce, Pig and Hive.
• Experienced in building highly scalable Big-data solutions using NoSQL column-oriented databases like Cassandra, MongoDB and HBase by integrating them with Hadoop Cluster.
• Manager and SaaS, PaaS and IaaS concepts of Cloud Computing and Implementation Worked with
• Extensive work on ETL processes consisting of data transformation, data sourcing, mapping, conversion and loading data from heterogeneous systems like flat files, Excel, Oracle, Teradata, MSSQL Server.
• Experience of building ETL production pipelines using Informatica Power Center, SSIS, SSAS, SSRS.
• Proficient at writing MapReduce jobs and UDF’s to gather, analyze, transform, and deliver the data as per business requirements and optimizing the existing algorithms for best results.
• Experience in working with Data warehousing concepts like Star Schema, Snowflake Schema, DataMarts, Kimball Methodology used in Relational and Multidimensional data modeling.
• Strong experience leveraging different file formats like Avro, ORC, Parquet, JSON and Flat files.
• Sound knowledge on Normalization and De-normalization techniques on OLAP and OLTP systems.
• Good experience with Version Control tools Bitbucket, GitHub, GIT.
• Experience with Jira, Confluence and Rally for project management and Oozie, AirFlow scheduling tools.
• Experienced in Strong scripting skills in Python, Scala and UNIX shell.
• Involved in writing Python, Java API’s for Amazon Lambda functions to manage the AWS services.
• Experience in design, development and testing of Distributed Client/Server and Database applications using Java, Spring, Hibernate, Struts, JSP, JDBC, REST services on Apache Tomcat Servers.
• Hands on working experience with RESTful API’s, API life cycle management and consuming RESTful services
• Have good working experience in Agile/Scrum methodologies, communication with scrum calls for project analysis and development aspects.
• Worked with Google Cloud(GCP) Services like Compute Engine, Cloud Functions, Cloud DNS, Cloud Storage and Cloud Deployment Manager and SaaS, PaaS and IaaS concepts of Cloud Computing and Implementation using GCP

TECHNICAL SKILLS

Programming Languages: Python, Scala, SQL, Java, C/C++, Shell Scripting

Web Technologies: HTML, CSS, XML, AJAX, JSP, Servlets, JavaScript

Big Data Stack: Hadoop, Spark, MapReduce, Hive, Pig, Yarn, Sqoop, Flume, Oozie, Kafka, Impala, Storm

Cloud Platform: Amazon Web Services(AWS), Google Cloud Platform (GCP)Relational databases Oracle, MySQL, SQL Server, PostgreSQL, Teradata, Snowflake

NoSQL databases: MongoDB, Cassandra, HBase, Pig

Version Control Systems: Bitbucket, GIT, SVN, GitHub

IDEs: PyCharm, Intellij IDEA, Jupyter Notebooks, Google Colab, Eclipse

Operating Systems: Unix, Linux, Windows

PROFESSIONAL EXPERIENCE

Confidential, Philadelphia, PA

Sr Data Engineer

Responsibilities:

• Worked on building the data pipelines (ELT/ETL Scripts), extracting the data from different sources (MySQL, AWS S3 files), transforming and loading the data to the Data Warehouse (AWS Redshift)
• Worked on adding the Rest API layer to the ML models built using Python,Flask& deploying the models in AWS BeanStalk Environment using Docker containers
• Worked on developing & adding few Analytical dashboards using Looker product
• Worked on building the aggregate tables & de-normalized tables, populating the data using ETL to improve the looker analytical dashboard performance and to help data scientist and analysts to speed up the ML model training & analysis
• Created New Dashboards, reports, scheduled searches and alerts using spunk
• Integrated Pager duty with Splunk to generate the Incidents from Splunk
• Developed custom Jenkins jobs/pipelines that contained Bash shell scripts utilizing the AWS CLI to automate infrastructure provisioning
• Developed a user-eligibility library using Python to accommodate the partner filters and exclude these users from receiving the credit products
• Built the data pipelines to aggregate the user click stream session data using spark streaming module which reads the click stream data from Kinesis streamsand store the aggregate results in S3 and data and eventually loaded to AWS Redshift warehouse
• Worked on supporting & building the infrastructure for the core module of the Credit Sesame i.e Approval Odds, started with Batch ETL, moved to micro-batches and then converted to a real time predictions
• Developed the AWS Lambda server less scripts to handle ad-hoc requests
• Performed Cost optimization reduced the infrastructure costs
• Knowledge and experience on using Python Numpy, Pandas, Sci-kit Learn, Onnx& Machine Learning
• Worked on building the data pipelines using PySpark ( AWS EMR), processing the data files present in S3 and loading it to Redshift
• Other activities include supporting and keeping the data pipelines active, working with Product Managers, Analysts, Data Scientist & addressing the requests coming from them, unit testing, load testing and SQL optimizations

Environment: Groovy, Python, Flask, Numpy, Pandas, SQL, MySQL, Cassandra, AWS EMR, Spark, AWS Kinesis, AWS Redshift, AWS EC2, AWS S3, AWS BeanStalk, AWS Lambda, AWS data pipeline, AWS cloud-watch, Docker, Shell scripts, Looker.

Confidential, DALLAS,TX

Sr Data Engineer

Responsibilities:

• As a Data Engineer I am responsible for building scalable distributed data solutions using Hadoop.
• Involved in Agile Development process (Scrum and Sprint planning).
• Handled Hadoop cluster installations in Windows environment.
• Migrated on-premise environment in GCP (Google Cloud Platform)
• Migrated data warehouses to Snowflake Data warehouse.
• Defined virtual warehouse sizing for Snowflake for different type of workloads.
• Involved in porting the existing on-premise Hive code migration to GCP (Google Cloud Platform) BigQuery.
• Worked with Google Cloud (GCP) Services like Compute Engine, Cloud Functions, Cloud DNS, Cloud Storage and Cloud Deployment Manager and SaaS, PaaS and IaaS concepts of Cloud Computing and Implementation using GCP.
• Involved in migration an Oracle SQL ETL to run on Google cloud platform using cloud Dataproc&BigQuery, cloud pub/sub for triggering the Apache Airflow jobs.
• Extracted data from data lakes, EDW to relational databases for analyzing and getting more meaningful insights using SQL Queries and PySpark.
• Developed PySpark script to merge static and dynamic files and cleanse the data.
• Created Pyspark procedures, functions, packages to load data.
• Designed, developed and did maintenance of data integration programs in a Hadoop and RDBMS environment with both traditional and non-traditional source systems.
• Developed MapReduce programs to parse the raw data, populate staging tables and store the refined data in partitioned tables in the EDW.
• Wrote Sqoop Scripts for importing and exporting data from RDBMS to HDFS.
• Set up Data Lake in Google cloud using Google cloud storage, BigQuery and Big Table.
• Developed scripts in BigQuery and connecting it to reporting tools.
• Designed workflows using Airflow to automate the services developed for Change data capture.
• Carried out data transformation and cleansing using SQL queries and PySpark.
• Used Kafka and Spark streaming to ingest real time or near real time data in HDFS.
• Worked related to downloading BigQuery data into Spark data frames for advanced ETL capabilities.
• Worked on PySpark APIs for data transformations.
• Built reports for monitoring data loads into GCP and drive reliability at the site level.
• Participated in daily stand-ups, bi-weekly scrums and PI panning.

Environment: Hadoop 3.3, GCP, BigQuery, Big Table, Spark 3.0, PySpark, Sqoop 1.4.7, ETL, HDFS, Snowflake DW, Oracle Sql, MapReduce, Kafka 2.8 and Agile process.

Confidential

Sr Data Engineer

Responsibilities:

• Extensive experience in working with AWS cloud Platform (EC2, S3, EMR, Redshift, Lambda and Glue).
• Working knowledge of Spark RDD, Data Frame API, Data set API, Data Source API, Spark SQL and Spark Streaming.
• Developed Spark Applications by using Python and Implemented Apache Spark data processing Project to handle data from various RDBMS and Streaming sources.
• Worked with the Spark for improving performance and optimization of the existing algorithms in Hadoop.
• Using Spark Context, Spark-SQL, Spark MLlib, Data Frame, Pair RDD and Spark YARN.
• Used Spark Streaming APIs to perform transformations and actions on the fly for building common.
• Learner data model which gets the data from Kafka in real time and persist it to Cassandra.
• Developed Kafka consumer API in python for consuming data from Kafka topics.
• Consumed Extensible Markup Language (XML) messages using Kafka and processed the XML file using Spark Streaming to capture User Interface (UI) updates.
• Developed Preprocessing job using Spark Data frames to flatten JSON documents to flat file.
• Load D-Stream data into Spark RDD and do in memory data Computation to generate output response.
• Experienced in writing live Real-time Processing and core jobs using Spark Streaming with Kafka as a Data pipeline system.
• Migrated an existing on-premises application to AWS. Used AWS services like EC2 and S3 for data sets processing and storage.
• Experienced in Maintaining the Hadoop cluster on AWS EMR.
• Loaded data into S3 buckets using AWS Glue and PySpark. Involved in filtering data stored in S3 buckets using Elasticsearch and loaded data into Hive external tables.
• Configured Snow pipe to pull the data from S3 buckets into Snowflakes table.
• Stored incoming data in the Snowflakes staging area.
• Created numerous ODI interfaces and load into Snowflake DB.
• Worked on Amazon Redshift for shifting all Data warehouses into one Data warehouse.
• Good understanding of Cassandra architecture, replication strategy, gossip, snitches etc.
• Designed columnar families in Cassandra and Ingested data from RDBMS, performed datatransformations, and then exported the transformed data to Cassandra as per the business requirement.
• Used the Spark Data Cassandra Connector to load data to and from Cassandra.
• Worked from Scratch in Configurations of Kafka such as Mangers and Brokers.
• Experienced in creating data-models for Clients transactional logs, analyzed the data from Cassandra.
• Tables for quick searching, sorting and grouping using the Cassandra Query Language.
• Tested the cluster performance using Cassandra-stress tool to measure and improve the Read/Writes.
• Used Hive QL to analyze the partitioned and bucketed data, Executed Hive queries on Parquet tables.
• Stored in Hive to perform data analysis to meet the business specification logic.
• Used Apache Kafka to aggregate web log data from multiple servers and make them available inDownstream systems for Data analysis and engineering type of roles.
• Worked in Implementing Kafka Security and Boosting its performance.
• Experience in using Avro, Parquet, RCFile and JSON file formats, developed UDF in Hive.
• Developed Custom UDF in Python and used UDFs for sorting and preparing the data.
• Worked on Custom Loaders and Storage Classes in PIG to work on several data formats like JSON,XML, CSV and generated Bags for processing using pig etc.
• Developed Sqoop and Kafka Jobs to load data from RDBMS, External Systems into HDFS and HIVE.
• Developed Oozie coordinators to schedule Hive scripts to create Data pipelines.
• Written several Map Reduce Jobs using Pyspark, Numpy and used Jenkins for Continuous integration.
• Setting up and worked on Kerberos authentication principals to establish secure network communication.
• On cluster and testing of HDFS, Hive, Pig and MapReduce to access cluster for new users.
• Continuous monitoring and managing the Hadoop cluster through Cloudera Manager.

Environment: Spark, Spark-Streaming, Spark SQL, AWS EMR, map R, HDFS, Hive, Pig, Apache Kafka,Sqoop, Python, Pyspark, Shell scripting, Linux, MySQL Oracle Enterprise DB, SOLR, Jenkins,Eclipse, Oracle, Git, Oozie, Tableau, MySQL, Soap, Cassandra and Agile Methodologies.

Confidential

Big Data Engineer

Responsibilities:

• Participate in requirement grooming meetings which involves understanding functional requirements from business perspective and providing estimates to convert those requirements into software solutions (Design and Develop & Deliver the Code to IT/UAT/PROD and validate and manage data Pipelines from multiple applications with fast-paced Agile Development methodology using Sprints with JIRA Management Tool)
• Responsible to check data in DynamoDB tables and to check EC2 instances are upon running for
• (DEV, QA, CERT and PROD) in AWS.
• Analysis on existing data flows and create high level/low level technical design documents for business stakeholders that confirm technical design aligns with business requirements.
• Creation and deployment of Spark jobs in different environments and loading data to no sql database Cassandra/Hive/HDFS. Secure the data by implementing encryption-based
• Implemented AWS solutions using E2C, S3, RDS, EBS, Elastic Load Balancer, Auto scaling groups, Optimized volumes, and EC2 instances and created monitors, alarms, and notifications for EC2 hosts using Cloud Watch.
• Developing code using Apache Spark and Scala, IntelliJ, NoSQL databases (Cassandra), Jenkins, Docker pipelines, GITHUB, Kubernetes, HDFS file System, Hive, Kafka for streaming Real time streaming data, Kibana for monitor logs etc. authentication/authorization to the data Responsible to deployments to DEV, QA, PRE-PROD (CERT) and PROD using AWS.
• Scheduled Informatica Jobs through Autosys scheduling tool.
• Created quick Filters Customized Calculations on SOQL for SFDC queries, Used Data loader for ad hoc data loads for Salesforce
• Extensively worked on Informatica power center Mappings, Mapping Parameters, Workflows, Variables and Session Parameters.
• Responsible for facilitating load data pipelines and benchmarking the developed product with the set performance standards.
• Used Debugger within the Mapping Designer to test the data flow between source and target and to troubleshoot the invalid mappings.
• Worked on SQL tools like TOAD and SQL Developer to run SQL Queries and validate the data.
• Study the existing system and conduct reviews to provide a unified review on jobs.
• Involved in Onsite & Offshore coordination to ensure the deliverables.
• Involving in testing the database using complex SQL scripts and handling the performance issues effectively.

Environment: Apache spark 2.4.5, Scala2.1.1, Cassandra, HDFS, Hive, GitHub, Jenkins, kafka, SQL Server 2008, Salesforce Cloud, Visio, TOAD, Putty, Autosys Scheduler, UNIX, AWS, WinScp, Salesforce data loader, SFDC Developer console.

Confidential

Bigdata Engineer

Responsibilities:

• Imported the data from various formats like JSON, Sequential, Text, CSV, AVRO and Parquet to HDFS cluster with compressed for optimization.
• Worked on ingesting data from RDBMS sources like - Oracle, SQL Server and Teradata into HDFS using Sqoop.
• Loaded all datasets into Hive from Source CSV files using Spark and Cassandra from Source CSV files using Spark
• Created environment to access Loaded Data via Spark SQL, through JDBC&ODBC (via Spark Thrift Server).
• Developed real time data ingestion/ analysis using Kafka / Spark-streaming.
• Configured Hive and written Hive UDF's and UDAF's Also, created Static and Dynamic with bucketing as required.
• Worked on writing Scala programs using Spark on Yarn for analyzing data.
• Managing and scheduling Jobs on a Hadoop cluster using Oozie.
• Created Hive External tables and loaded the data into tables and query data using HQL.
• Written Hive jobs to parse the logs and structure them in tabular format to facilitate effective querying on the log data.
• Developed Oozie workflow for scheduling and orchestrating the ETL process and worked on Oozie workflow engine for job scheduling.
• Managed and reviewed the Hadoop log files using Shell scripts.
• Migrated ETL jobs to Pig scripts to do transformations, even joins and some pre-aggregations before storing the data onto HDFS.
• Using Hive join queries to join multiple tables of a source system and load them to Elastic search tables.
• Real time streaming, performing transformations on the data using Kafka and Kafka Streams.
• Built NiFidataflow to consume data from Kafka, make transformations on data, place in HDFS & exposed port to run Spark streaming job.
• Developed Spark Streaming Jobs in Scala to consume data from Kafkatopics, made transformations on data and inserted to HBase.
• Implemented Spark using Scala and SparkSQL for faster testing and processing of data.
• Experience in managing and reviewing huge Hadoop log files.
• Collected the logs data from web servers and integrated in to HDFS using Flume.
• Expertise in designing and creating various analytical reports and Automated Dashboards to help users to identify critical KPIs and facilitate strategic planning in the organization.
• Involved in Cluster maintenance, Cluster Monitoring and Troubleshooting.
• Worked with Avro Data Serialization system to work with JSON data formats.
• Used Amazon Web Services (AWS) S3 to store large amount of data in identical/similar repository.

Environment: Spark, Spark SQL, Spark Streaming, Scala, Kafka, Hadoop, HDFS, Hive, Oozie, Pig, Nifi, Sqoop, AWS (EC2, S3, EMR), Shell Scripting, HBase, Jenkins, Tableau, Oracle, MySQL, Teradata and AWS.

Confidential

Python Developer

Responsibilities:

• Involved in the Design, development, test, deploy and maintenance of the website.
• Developed entire frontend and backend modules using Python.
• Developed application for Google Analytics aggregation and reporting.
• Responsible for debugging the project monitored on JIRA (Agile).
• Developed Python batch processors to consume and produce various feeds.
• Worked on building the data pipelines performed transformations on this extracted data and loaded it to MySQL database using python
• Generated PDF daily and Monthly statements using Aspose PDF Kit.
• Implemented Test Driven Development (TDD) strategy for the project.
• Using Subversion version control tool to coordinate team-development.
• Generated property list for every application dynamically using Python.
• Responsible for search engine optimization to improve the visibility of the website.
• Wrote validation scripts in SQL to validate data loading.
• Performed Unit and system testing.

Environment: Python, Django, MySQL, Git, Linux.