big data engineer/data engineer
Resume:
Trisha
Data Engineer / Big Data Engineer
***********@*****.***
Summary:
Overall, 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 Google Cloud (GCP) Services like Compute Engine, Cloud Functions, Cloud DNS, Cloud Storage and Cloud Deployment using GCP.
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.
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.
Used AWS IAM, Kerberos and Ranger for security compliance.
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.
Good Knowledge in building interactive dashboards, performing ad-hoc analysis, generating reports and visualizations using Tableau and PowerBI.
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.
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 AWS, EC2, EC3, MS Azure, Azure SQL Database, Google Cloud Services (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:
Pfizer, New York City NY Apr 2021 – present
Role: Sr 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.
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.
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, Informatica PowerCenter 10.x, SQL Server 2008, Salesforce Cloud, Visio, TOAD, Putty, Autosys Scheduler, UNIX, AWS, GCP, WinScp, Salesforce data loader, SFDC Developer console, Version One, Service Now etc.
Walgreens, Chicago IL May 2019 – Mar 2021
Role: 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 data transformations, 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 in Downstream 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.
Cardinal Health, Dublin, Ohio Apr 2017 – Apr 2019
Role: Big Data Engineer
Responsibilities:
Worked as a Sr. Big Data Engineer with Hadoop Ecosystems components like HBase, Sqoop, Zookeeper, Oozie, Hive and Pig with Cloudera Hadoop distribution.
Involved in Agile development methodology active member in scrum meetings.
Worked in Azure environment for development and deployment of Custom Hadoop Applications.
Designed and implemented scalable Cloud Data and Analytical architecture solutions for various public and private cloud platforms using Azure.
Involved in start to end process of Hadoop jobs that us ed various technologies such as Sqoop, PIG, Hive, MapReduce, Spark, and Shells scripts.
Implemented various Azure platforms such as Azure SQL Database, Azure SQL Data Warehouse, Azure Analysis Services, HDInsight, Azure Data Lake and Data Factory.
Extracted and loaded data into Data Lake environment (MS Azure) by using Sqoop which was accessed by business users.
Manage and support of enterprise Data Warehouse operation, big data advanced predictive application development using Cloudera & Hortonworks HDP.
Developed PIG scripts to transform the raw data into intelligent data as specified by business users.
Utilized Apache Spark with Python to develop and execute Big Data Analytics and Machine learning applications, executed machine learning use cases under Spark ML and MLLib.
Installed Hadoop, Map Reduce, HDFS, Azure to develop multiple MapReduce jobs in PIG and Hive for data cleansing and pre-processing.
Used Spark API over Hortonworks Hadoop YARN to perform analytics on data in Hive.
Improved the performance and optimization of the existing algorithms in Hadoop using SparkContext, Spark-SQL, Data Frame, Pair RDD's, Spark YARN.
Developed Spark code using Scala and Spark-SQL/Streaming for faster testing and processing of data.
Developed a Spark job in Java which indexes data into Elastic Search from external Hive tables which are in HDFS.
Performed transformations, cleaning and filtering on imported data using Hive, MapReduce, and loaded final data into HDFS.
Explored with the Spark improving the performance and optimization of the existing algorithms in Hadoop using Spark Context, Spark-SQL, Data Frame, Pair RDD's, Spark YARN.
Import the data from different sources like HDFS/HBase into Spark RDD and developed a data pipeline using Kafka and Storm to store data into HDFS.
Used Spark streaming to receive real time data from the Kafka and store the stream data to HDFS using Scala and NoSQL databases such as HBase and Cassandra.
Documented the requirements including the available code which should be implemented using Spark, Hive, HDFS, HBase and Elastic Search.
Performed transformations like event joins, filter boot traffic and some pre-aggregations using Pig.
Explored MLLibalgorithms in Spark to understand the possible Machine Learning functionalities that can be used for our use case
Used windows Azure SQL reporting services to create reports with tables, charts, and maps.
Executed Hive queries on Parquet tables stored in Hive to perform data analysis to meet the business requirements.
Configured Oozie workflow to run multiple Hive and Pig jobs which run independently with time and data availability.
Imported and exported the analyzed data to the relational databases using Sqoop for visualization and to generate reports for the BI team.
Environment: Hadoop 3.0, Azure, Sqoop 1.4.6, PIG 0.17, Hive 2.3, MapReduce, Spark 2.2.1, Shells scripts, SQL, Hortonworks, Python, MLLib, HDFS, YARN, Java, Kafka 1.0, Cassandra 3.11, Oozie, Agile
State Farm, Bloomington, Illinois Feb 2016 – Mar 2017
Role: 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.
Worked with the Data Science team to gather requirements for various data mining projects.
Automated the process of rolling day-to-day reporting by writing shell scripts.
Involved in build applications using Maven and integrated with Continuous Integration servers like Jenkins to build jobs.
Worked on BI tools as Tableau to create dashboards like weekly, monthly, daily reports using tableau desktop and publish them to HDFS cluster.
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.
Client: Genpact May 2012 – Dec 2014
Location: India
Role: Data Analyst
Responsible for gathering requirements from Business Analyst and Operational Analyst and identifying the data sources required for the request.
Worked closely with a data architect to review all the conceptual, logical and physical database design models with respect to functions, definition, maintenance review and support data analysis, Data quality and ETL design that feeds the logical data models.
Maintained and developed complex SQL queries, stored procedures, views, functions, and reports that qualify customer requirements using SQL Server 2012.
Creating automated anomaly detection systems and constant tracking of its performance.
Support Sales and Engagement's management planning and decision making on sales incentives.
Used statistical analysis, simulations, predictive modelling to analyze information and develop practical solutions to business problems.
Extending the company's data with third-party sources of information when needed.
Précised development of several types of sub-reports, drill down reports, summary reports, parameterized reports, and ad-hoc reports using SSRS through mailing server subscriptions &SharePoint server.
Generated ad-hoc reports using Crystal Reports 9 and SQL Server Reporting Services (SSRS).
Developed the reports and visualizations based on the insights mainly using Tableau and dashboards for the company insight teams.
Environment: SQL Server 2012, SSRS, SSIS, SQL Profiler, Tableau, Qlik View, Agile, ETL, Anomaly detection.
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