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Adhvayth
adhvaythreddyangula@gmail.com
469-844-1692
Washington, DC 20440
Senior Azure
14 years experience W2
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Summary

  • Around 10+ years of professional experience in the Information Technology sector with expertise in Big Data, Hadoop, Spark, Map-Reduce, Hive, Flask, Impala, Sqoop, Flume, Kafka, SQL, ETL development, report development, database development, and data modelling.
  • Proficient in multiple databases such as PostgreSQL, NoSQL databases (MongoDB, Cassandra), MySQL, ORACLE, and MS SQL Server. Adept at migrating SQL databases to Azure Data Lake, Azure SQL Database, Databricks, and Azure SQL Data Warehouse.
  • Hands-on experience in building data pipelines using AWS services like S3, Redshift, Glue, Lambda functions, CloudWatch, SNS, DynamoDB and StreamSets.
  • Experienced in developing custom UDFs for Pig and Hive to incorporate Python/Java functionality into Pig Latin and HQL.
  • Implement test data to serve various IT environments (DEV, QA, UAT).
  • Strong understanding of Big Data architecture, including Hadoop (Azure, Hortonworks, and Cloudera) distributed systems, MongoDB, NoSQL, and HDFS.
  • Proven expertise in programming languages like Python, core Java, JDBC, JavaScript, and using Java APIs for application development.
  • Proficient in data visualization and analysis using Tableau and Power BI on large datasets, drawing valuable conclusions.
  • Exposure to AI and Deep learning platforms such as TensorFlow, Keras, AWS ML, Azure ML studio.
  • Very keen on knowing the newer techno stack that Google Cloud platform (GCP) adds.
  • Developed custom alerts using Azure Data factory, SQLDB and Logic apps.
  • Skilled in utilizing Google Cloud Storage and Big Query applications integrated with Tableau for web-based dashboards and reports.
  • Develop and monitor IBM DataStage Jobs using various Processing and Debug Stages.
  • Exposure to AI and Deep learning platforms/methodologies like TensorFlow, RNN, LSTM.
  • Experience in building data visualizations in Python and dashboards in Powerbase.
  • Experienced in collaborating with the data migration team to migrate data from on-premises SQL clusters to the Azure cloud using Azure Data Factory.
  • Extensive knowledge of enterprise-level data intake pipelines, data transformations, data governance, and real-time streaming.
  • Implemented various components in OLAP systems using different ETL tools like OWB, Wherescape RED, Pentaho, Informatica PWC.
  • Experience in developing end-to-end ETL pipelines using Snowflake, Alteryx, Apache NiFi for both relational and non-relational databases (SQL and NoSQL).
  • Good knowledge of Hadoop cluster architecture and its key concepts
  • Distributed file systems, Parallel processing, High availability, fault tolerance, Optimized MapReduce algorithms, Scalability.
  • Complete knowledge of Hadoop architecture and Daemons of Hadoop clusters, which include Name node, Data node, Resource manager, Node Manager, Job history server.
  • Implement Airflow operators to execute tasks such as data extraction, transformation, and loading (ETL).
  • Strong team player with excellent communication and analytical skills, ensuring effective collaboration and problem-solving.
  • Can work parallel in both GCP and Azure Clouds coherently.
  • Provided leadership and mentorship to junior team members, fostering a collaborative and knowledge-sharing environment. Conducted training sessions on emerging technologies, best practices, efficient coding standards, and DBT (Data Build Tool) to enhance the team's overall skill set.
  • Implemented robust data quality assurance processes to ensure the accuracy, consistency, and reliability of large datasets, resulting in improved overall data integrity and trustworthiness.
  • Implemented comprehensive security measures for sensitive data, including encryption protocols, access controls, and data masking techniques, ensuring compliance with industry regulations and safeguarding against unauthorized access.
  • Strong working experience on NoSQL databases and their integration with the Hadoop cluster
  • HBase, Cassandra, MongoDB, DynamoDB, CosmosDB.
  • Utilized StreamSets for comprehensive data quality assurance, real-time monitoring, and seamless integration with various data sources and destinations.
  • Utilize Airflow DAGs (Directed Acyclic Graphs) to define workflows and dependencies between tasks.
  • Experience with AWS cloud services to develop cloud-based pipelines and Spark applications using EMR, LAMBDA, Redshift.
  • Developed normalized Logical and Physical database models to design OLTP system.
  • Led initiatives for scalability and capacity planning, effectively forecasting and addressing resource requirements to accommodate growing data volumes, thereby optimizing infrastructure utilization and minimizing downtime.
  • Successfully managed reporting analytics infrastructure for internal business needs.
  • Strong expertise in SQL Server Reporting Services, Analysis Services, data visualization tools.
  • In-depth understanding of Spark Architecture, Databricks, Structured Streaming.
  • Proficient in working with different scripting technologies like Python, UNIX shell scripts.
  • Hands-on experience with Amazon EMR, Spark, Kinesis, S3, ECS, Elastic Cache, DynamoDB.
  • Excellent grasp of Apache Hadoop for analyzing big data using MapReduce.
  • Experience in Image Classification, Object Detection using CNN, Deep Learning
  • TensorFlow, Pytorch.
  • Skilled in creating Snowflake Schemas and normalizing data from dimensions for enhanced data organization, with integration of DBT (Data Build Tool).
  • Experienced in designing and developing ETL processes using Talend Integration Suite and DBT (Data Build Tool).

Experience
Education
Skills
Information Technology
2024
10
Azure SQL Database
2024
9
DataStage
2024
8
Hbase
2024
8
Big Data
2024
7
Data Visualization
2024
7
Flume
2024
7
Functional
2024
7
Java
2024
7
JavaScript
2024
7
Kafka
2024
7
OLAP
2024
7
Project Management
2024
7
Regulatory Reporting
2024
7
Shell Scripts
2024
7
Talend Studio
2024
7
UNIX
2024
7
Cloudwatch
2024
6
Drawing
2024
6
Flask
2024
6
JDBC
2024
6
Loading
2024
6
Pentaho
2024
6
Quality Assurance
2024
6
Sqoop
2024
6
Training
2024
6
Utilization Review
2024
6
Cassandra
2024
5
Dimensions
2024
5
Scala
2024
4
ETL
2024
18
MS Azure
2025
18
Python
2024
15
SQL
2024
15
Apache
2024
13
Snowflake
2024
13
Data Migration
2024
12
MapReduce
2024
12
MongoDB
2024
12
Spark
2024
12
Informatica
2024
11
Oracle
2024
11
SQL Server
2024
11
Warehouse
2024
11
Data Lakes
2024
10
Microsoft Excel
2024
10
MySQL
2024
10
Scripting
2024
10
Tableau
2024
10
Hive
2024
9
OLTP
2024
9
Pipeline
2024
9
PostgreSQL
2024
9
AWS
2024
8
Data Integrity
2024
8
Pig
2024
8
MS Power BI
2024
7
QA
2024
7
Azure Storage
2025
6
Continuous Deployment
2025
6
PySpark
2020
6
Stored Procedure
2022
6
Azure Data Factory
2025
5
Data Engineering
2022
5
Data Mining
2017
5
Hadoop
2022
5
Git
2022
4
Performance Tuning
2020
4
Triggers
2020
4
AWS EMR
2020
3
Business Intelligence
2015
3
Data Access
2015
3
Data Modeling
2015
3
Data Warehousing
2015
3
Docker Containers
2022
3
HDFS
2022
3
impala
2020
3
SSAS
2015
3
SSAS CUbe
2015
3
SSIS
2015
3
SSRS
2015
3
Business Analysis
2017
2
Continuous Improvement
2022
2
Data Analysis
2022
2
Data Integration
2017
2
Data Management
2022
2
Eclipse
2022
2
Jenkins
2017
2
JSON
2022
2
PL/SQL
2022
2
Terraform
2017
2
WebServices
2022
2
AWS EC2
2020
1
AWS Lambda
2020
1
AWS Redshift
2020
1
AWS S3
2020
1
Tableau Server
2020
1