Involved in the data pipeline development and deployment as part of the Software Development Life Cycle (SDLC),ensuring data availability, accuracy, and timeliness.▪ Leveraged AWS services, including EC2, S3, Lambda, Step Functions, and Auto Scaling, to architect scalable andcost-effective data processing solutions in the cloud.▪ Implemented robust Identity and Access Management (IAM) policies to enforce data security and access controlsacross AWS resources.▪ Designed and managed data ingestion and transformation processes using AWS Glue, Python, and ETL bestpractices, ensuring data quality and consistency.▪ Orchestrated data workflows, error handling, and retries with AWS Step Functions, enhancing data pipelinereliability.▪ Utilized PostgreSQL and DynamoDB to store and manage structured and semi-structured data efficiently.▪ Developed and maintained RESTful APIs for data retrieval and integration with external systems, fostering dataaccessibility and collaboration.▪ Collaborated with Snowflake data warehouse, ensuring optimal data storage, querying, and performance for analytics.▪ Implemented infrastructure as code (IaC) using Terraform, automating the provisioning and management of cloudresources.▪ Proficiently handled data in various formats, including JSON and XML, to support diverse data sources andintegration requirements.▪ Utilized Hadoop and Spark for distributed data processing, employing Hive and Pig for data transformation andSqoop for data ingestion.▪ Orchestrated real-time data streaming and event processing using Kafka, enabling low-latency data insights.▪ Employed Elasticsearch for log and text data analysis, providing powerful search and analytics capabilities.▪ Managed source code version control using Git and automated the build and deployment pipelines with Jenkins.

Designed and implemented ETL processes using Informatica to efficiently extract, transform, and load data fromvarious sources into a centralized data repository.▪ Leveraged Python and SQL to develop custom data pipelines, ensuring data accuracy and consistency across multipledata sources.▪ Orchestrated data workflows in Azure Data Factory (ADF) and utilized Azure Databricks for data processing,demonstrating proficiency in Azure cloud technologies.▪ Contributed to developing Azure-based solutions, including Azure Data Warehouse (Azure DW) and Cosmos DB, tosupport scalable and high-performance data storage and retrieval.▪ Managed MySQL databases, optimizing queries and performance, and employed ARM templates for infrastructure ascode (IaC) in Azure environments.▪ Implemented Hadoop Distributed File System (HDFS) and utilized Hadoop ecosystem tools such as HDP, Hadoop,Spark, Hive, Pig, and Sqoop for Big Data processing and analysis.▪ Developed data pipelines using Scala, enabling efficient data transformation and manipulation in distributedcomputing environments.▪ Proficiently handled data in various formats, including XML and JSON, ensuring seamless integration with diversedata sources.▪ Managed source code version control using Bitbucket and orchestrated continuous integration/continuousdeployment (CI/CD) pipelines in Azure DevOps.▪ Containerized applications and services using Docker, streamlining deployment processes and enhancing scalability.▪ Implemented log analysis and monitoring solutions with Splunk, ensuring data quality and system reliability.▪ Created interactive and insightful data visualizations using Power BI, enabling stakeholders to make data-drivendecisions.▪ Collaborated with cross-functional teams and tracked project progress using JIRA in an Agile developmentenvironment.▪ Conducted data quality assessments and implemented data cleansing processes to maintain data integrity.

Participated in managing data in SQL Server databases. Gained exposure to Cassandra for NoSQL data storage.▪ Performed with Google Cloud services, including Google Cloud Storage. Using Google Cloud Dataflow and Pub/Subfor data streaming and processing.▪ Collaborated on data preparation using Dataprep and managed data clusters with DataProc.▪ Employed Cloudera Distribution for Hadoop (CDH), Hadoop, and Apache Spark for big data processing.▪ Worked with Hive and Pig for data warehousing and analytics.▪ Contributed to data analysis and querying in Google BigQuery.▪ Assisted in setting up Prometheus for data monitoring and alerting.▪ Contributed to automation pipelines using Jenkins for data workflows.▪ Collaborated on data visualization using Matplotlib for insights.▪ Utilized Git for version control of data-related code and scripts.▪ Worked with ANT for build automation. Worked on parsing and handling XML data for integration.▪ Assisted in integrating data with Service Now for tracking and management.Environment: ETL, Talend, Python, REST API, SQL Server, Cassandra, GCP, Google Cloud Storage, Dataflow,Pub/Sub, DataProc, Dataprep, CDH, Hadoop, Spark, Hive, Pig, BigQuery, Prometheus, Jenkins, Matplotlib, Git, ANT,XML, Service Now.

Gathered information from various sources like databases and files.▪ Cleaned and organized data to make it useful for analysis.▪ Worked with ETL (Extract, Transform, Load) tools like SSIS to move data smoothly.▪ Used Python and SQL to write code for data tasks.▪ Assisted in managing data in Oracle databases.▪ Employed NumPy and Pandas to explore and analyze data.▪ Utilized Excel for data manipulation and visualization.▪ Created data visualizations using Seaborn to help with understanding.▪ Assisted in using Splunk to monitor data and find insights.▪ Contributed to tracking and resolving issues in Bugzilla.Environment: ETL, SSIS, Python, SQL, PL/SQL, NumPy, Pandas, Excel, Oracle, Seaborn, Splunk, Bugzilla.