Data Engineer (AWS & Azure) Resume

Lavanya

Data Engineer

*******.*******@*****.***

978-***-****

Professional Summary:

• Capable of using AWS utilities such as EMR, S3, and cloud watch to run and monitor Hadoop and Spark jobs on AWS.

• Knowledge on scalable cloud-based web applications using AWS and Azure.

• Stayed up to date with the latest technologies and techniques in Data engineering and recommended changes to improve data pipeline efficiency and reliability

• Knowledge in Migrating SQL database to Azure Data Lake, Azure data lake Analytics, Azure SQL Database, Data Bricks, and Azure SQL Data Warehouse and controlling and granting database access and migrating on-premises Databases to Azure Data Lake store using Azure Data Factory.

• Knowledge in writing complex SQL Queries like Stored Procedures, triggers, joints, and Sub queries.

• Developed AWS CI/CD Data pipeline and AWS Data Lake using EC2, AWS Glue, and AWS Lambda.

• Ability to independently multi-task, be a self-starter in a fast-paced environment, communicate fluidly and dynamically with the team and perform continuous process improvements with out-of-the-box thinking.

• Knowledge in requirement analysis, application development, application migration, and maintenance using Software Development Lifecycle (SDLC) and Python/Java technologies.

Technical Skills:

Infrastructure as Code

CloudFormation, Terraform.

Continuous Integration

Jenkins, Travis, Bamboo, Splunk.

Build Tools

Ant, Maven, Gradle.

Version Control

Git, CVS, Subversion, Bitbucket, TFS

Cloud Platforms

AWS, Azure, GCP, OpenStack.

Database

RDS, Oracle 7.x/8.0/9i/10g/11g, MySQL, DynamoDB, MongoDB, Cassandra DB.

Programming Languages

.Net, C++.

Scripting

Python, Bash, Ruby, Groovy, Perl, Shell, HTML, JSON, YAML, XML.

Testing Tool

Selenium, Cucumber, SoapUI.

SDLC

Agile, Scrum, Waterfall, Kanban.

Education:

• Masters in Computer Applications – 2007

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