Sai Krishna Sarvadevabhatla

Data Engineer

Denton, Texas, United States1 yr 6 mos experience

Key Highlights

1.8 years of experience as a Cloud Data Engineer.
Expert in designing data processing pipelines.
Strong background in AWS and data engineering.

Stackforce AI infers this person is a Data Engineer with expertise in cloud computing and data processing.

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Skills

Core Skills

Data EngineeringCloud Computing

Other Skills

AWSAWS GlueAWS LambdaAd Hoc AnalysisAdobe AnalyticsAmazon Web Services (AWS)Analytical SkillsApache OozieApache SparkApplied MathematicsAutomationAzure Cosmos DBAzure Data LakeBusiness AnalysisBusiness Requirements

About

I am a Data Science graduate student at University of North Texas. Passionate developer with solid experience in Extract Transform and Load operations. I am motivated by the opportunity to design and develop solutions that create an impact. I have 1.8 years of experience as a Cloud Data Engineer in Value Momentum. Actively seeking for Full-time opportunities Skills and Technologies Python | C/C++ | Java| AWS | Github | JIRA | Snowflake| SQL | Data Mining | Exploratory Data Analysis | Object Oriented Programming

Experience

Valuemomentum

Data Engineer

Dec 2020 – Jul 2022 · 1 yr 7 mos · Hyderabad, Telangana, India

Roles and Responsibilities -
Design and implementation of MDM Pipeline to read complex JSON data coming from the MDM
sources, flatten the data, maintain active snapshots, history, incremental, SCD and Person xwalks
implementation.
Created the Pipeline using the Step function to create the EMR cluster, orchestrate the
Lambda/Python/Pyspark scripts, which processes huge amount of data.
Formulated Pyspark scripts for reading nested data from S3/Athena, unnest and generate the
processed files for respective tables.
Developed the Python script to read the latest processed files and load the data into Redshift stage
tables and load the data into the mart table after applying the SCD logic.
Worked on the design and implementation of Redshift SCD Queries.
Generated the synthetic data in large volumes which will help for the team to get the unit testing done
thoroughly.
Implemented AWS Managed Scaling Policy on AWS EMR which dynamically scales the cluster based on
the workload, thus making it cost efficient.
Upgraded the spark dynamic allocation mechanism to dynamically adjust the resources on application
occupies based on the workload, thus making time efficient.
Automated the process of Domain Model Upgradation which reduced the time of an individual in
doing the job manually.
Collaborated and supported Quality Assurance (QA) team in building functional scenarios and
validating results.