Raghu A. Email & Phone Number

Ashburn, Va, Us

Tysons Corner, Virginia, United States

•Implemented auto-scaling capabilities for containerized workloads on AWS Fargate, ensuring optimal resource utilization and cost efficiency.•Pre-processed raw data using Pandas to handle missing values, outliers, and duplicates, ensuring data quality and integrity.•Written python scripts integrating Boto3 to supplement automation provided by Ansible and Terraform for tasks such scheduling lambda functions for routine AWS tasks.•Experience in handling, configuration, and administration of databases like MySQL and NoSQL databases like MongoDB and DynamoDB.•Orchestrated Docker containers effectively using Amazon ECS (Elastic Container Service) with Fargate launch type, ensuring high availability and fault tolerance.•Employed Pandas and NumPy to perform exploratory data analysis, generating insights and visualizations to drive informed decision-making.•Implemented CI/CD pipelines using Azure DevOps, automating the build, testing, and deployment processes for cloud-native applications.•Working with snowflake query’s view and creating stages for unloading and loading data to the S3 bucket. •Developed Data ingestion modules into data to various layers in S3, Redshift and Snowflake using AWS kinesis, AWS lambda, AWS Glue, AWS Step Functions.•Performed SQL queries using AWS Athena to analyse data in S3 buckets.•Implemented feature engineering techniques in Sage maker notebooks, extracting and engineering relevant features from structured and unstructured data sources.•Optimized the PySpark jobs to run on Kubernetes Cluster for faster data processing.•Created Spark Streaming jobs using Python to read messages from Kafka & download JSON files from AWS S3 buckets.•Developed Data ingestion modules using AWS Glue, AWS Step Functions and Python Modules.

• Developed spark applications in Python using PySpark on distributed environment to load huge number of CSV files with different schema in to Hive ORC tables.• Experience developing powerful data visualisations using Microsoft Power bi, Tableau.• Experience working GraphQL APIs via client-side JavaScript or server side via Node.js. Developed web applications using react.js, jQuery. Used frameworks such as Bootstrap and Angular. • Implemented Ansible playbooks to automate configuration management tasks, such as provisioning servers, installing packages, and managing user accounts.• Experience in handling, configuration, and administration of databases like MySQL and NoSQL databases like MongoDB and Cassandra.• Performed end to end installation of PowerBI.• Automated ETL workflows and scheduling using tools like Apache Airflow or cron jobs, orchestrating complex data processing tasks and dependencies to streamline operations and minimize manual intervention.• Create a Pyspark frame to bring data from DB2 to Amazon S3.• Spearheaded the automation of infrastructure provisioning, configuration, and management processes using tools such as Terraform in a 30% reduction in deployment time and increased system reliability.• Optimize the Pyspark jobs to run on Kubernetes Cluster for faster data processing.• Worked very closely with designer, tightly integrating Flash into the CMS with the use of Flashovers stored in the Django models. Also created XML with Django to be used by the Flash.• Created Spark Streaming jobs using Python to read messages from Kafka & download JSON files from AWS S3 buckets.• Developed Data ingestion modules using AWS Glue, AWS Step Functions and Python Modules.• Data Modelling using Composite models in Power Bi, Retreived data from Rest Elastic Search and SQL Server • Access Fast API through a REST API to call common building blocks for an application• Used Ansible, Vagrant, and Docker for managing the application environments.

El Dorado, Arkansas, United States

• Used Python extract, transform, and aggregate data from multiple source systems for business use cases.• Seamlessly integrated diverse data sources, including databases, flat files, and APIs, utilizing SSIS data connections and source components.• Ensured data consistency and integrity while extracting sales data from multiple branch locations.• Leveraged SSIS transformations to cleanse and standardize raw sales data, employing conditional expressions and derived columns for improved data quality.• Implemented data validation routines to identify and rectify anomalies during the transformation process.• Designed an automated SSIS package that incorporated control flow tasks, data transformation steps, and event handlers to manage exceptions.• Utilized workflow automation tools like Microsoft Flow to trigger ETL execution based on predefined events or schedules.• Employed SSIS parallelism, data buffering, and indexing strategies to optimize ETL performance, significantly reducing processing time.• Employed Informatica's data cleansing transformations to remove duplicates, correct inconsistencies, and validate data integrity, ensuring high-quality data in target systems.

South San Francisco, California, United States

• Experience in building and architecting multiple Data pipelines, end to end ETL and ELT process for Data ingestion and transformation in GCP • Strong understanding of AWS components such as EC2 and S3• Implemented a Continuous Delivery pipeline with Docker and Git Hub • Worked with g-cloud function with Python to load Data in to BigQuery for on arrival csv files in GCS bucket• Process and load bound and unbound Data from Google pub/sub topic to BigQuery using cloud Dataflow with Python.• Devised simple and complex SQL scripts to check and validate Dataflow in various applications.• Performed Data Analysis, Data Migration, Data Cleansing, Transformation, Integration, Data Import, and Data Export through Python.• Developed and deployed cloud-based solutions on Microsoft Azure, leveraging services such as Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, and Azure Cosmos DB for data storage, processing, and management.• Developed Spark/Scala, Azure for regular expression (regex) project in the Databricks for big data resources. • Developed and deployed data pipeline in cloud such as AWS and GCP.• Designed and optimized data warehouse solutions on Azure, utilizing Azure Synapse Analytics (formerly SQL Data Warehouse) for scalable and high-performance analytics and reporting.• Performed data engineering functions: data extract, transformation, loading, and integration in support of enterprise data infrastructures – data warehouse, operational data stores and master data management• Responsible for data services and data movement infrastructures• Good experience with ETL concepts, building ETL solutions and Data modelling.

•Designed and implemented SQL scripts to map legacy data fields to corresponding fields in the new CRM system, accommodating data format differences.•Employed SQL's data transformation capabilities to cleanse, validate, and standardize data during migration.•Developed an Extract, Transform, Load (ETL) process using SQL's integration capabilities to extract data from the legacy system, transform it according to the new schema, and load it into the CRM platform.•Automated data extraction and reporting processes using Python scripts and AWS Lambda, improving efficiency and reducing turnaround time for ad hoc data requests.•Created custom SQL procedures and functions to handle complex data transformations and ensure data integrity.•Developed end-to-end ETL pipelines using Azure Data Factory to automate data ingestion, transformation, and loading processes, improving data processing efficiency.•Implemented SQL constraints and validations to maintain data quality during migration, preventing the introduction of incorrect or incomplete data into the new system.•Designed and executed data cleansing routines to address duplicate records and inconsistencies within the legacy dataset.•Optimized SQL queries and data loading processes to enhance migration speed and efficiency, leveraging SQL's indexing and query optimization techniques.•Developed SQL scripts to archive legacy data securely, ensuring historical records were preserved for compliance and historical analysis.