Ram Kumar

Designed and implemented scalable data pipelinesusing Azure Data Factory and Azure Databricks (Python, PySpark), optimizing ETL processes to handle large datasets from diverse sources, improving data processing times by 30%.

Developed and automated end-to-end data workflowsleveraging Azure Data Factory, enabling seamless data ingestion, transformation, and storage in Azure Data Lake, ensuring 99.9% availability and reliability.

Built complex ETL solutions using PySpark in Azure Databricks, improving data aggregation and transformation performance, leading to a 40% increase in processing efficiency.

Led migration efforts for on-premises data pipelines to Azure cloud environment, utilizing Azure Data Factory and Databricks, reducing infrastructure costs by 25%.

Implemented CI/CD pipelines in Azure DevOps for continuous integration and deployment of Azure Databricks notebooks and data pipeline code, reducing deployment time by 50% and improving code quality with automated testing.

Designed and managed Kafka topics and partitions to ensure fault-tolerant and scalable data streams, improving the reliability and performance of data flows across distributed systems.

Developed real-time data streaming pipelines using Azure Event Hub to ingest large-scale event data from various sources, enabling real-time analytics and event-driven architectures.

Collaborated with cross-functional teams to design and maintain data lake architectures using Azure Data Lake and Databricks, ensuring data governance, security, and compliance across multiple environments.

Optimized data transformation logic using PySpark in Databricks to reduce data processing costs by leveraging Azure Auto Scaling capabilities, reducing compute costs by 20%.

Developed monitoring and alerting systems using Azure Monitor and Log Analytics for proactive identification of data pipeline failures, reducing downtime by 15% through faster incident resolution.

Managed and orchestrated data workflows using Azure Data Factory, ensuring smooth integration of multiple data sources such as SQL Server, Blob Storage, and REST APIs.

Performed data validation and quality checks using PySpark scripts in Azure Databricks, ensuring high accuracy and consistency in data processing and reporting pipelines.

Developed and implemented a robust Data Quality frameworkto ensure data integrity, consistency, and accuracy across various data pipelines, automating validation processes for detecting anomalies and improving data reliability.

Designed and built data pipelines in Azure Data Factory (ADF) for efficient ETL/ELT processes, orchestrating workflows to ingest, transform, and load large datasets from multiple sources into cloud-based storage and analytics platforms.

Developed and Automated CI/CD pipelines using Azure DevOps, streamlining the deployment of data pipelines, reducing manual interventions and enhancing the speed and consistency of deployments.

Ensured continuous data delivery by integrating Azure Data Factory with other services such as Databricks, Azure SQL, and Synapse Analytics, optimizing performance and reducing data processing times.

Collaborated with cross-functional teams to integrate data from diverse sources, improving data accessibility and scalability for downstream analytics and reporting.

Designed and implemented scalable data pipelinesusing Azure Data Factory and Azure Databricks (Python, PySpark), optimizing ETL processes to handle large datasets from diverse sources.

Built complex ETL solutions using PySpark in Azure Databricks, improving data aggregation and transformation performance, leading to a 40% increase in processing efficiency.

Migration efforts for on-premises data pipelines to Azure cloud environment, utilizing Azure Data Factory and Databricks, reducing infrastructure costs by 25%.

Implemented CI/CD pipelines in Azure DevOps for continuous integration and deployment of Azure Databricks notebooks and data pipeline code, reducing deployment time by 50% and improving code quality with automated testing.

Developed real-time data streaming pipelines using Azure Event Hub to ingest large-scale event data from various sources, enabling real-time analytics and event-driven architectures.

Designed and managed Kafka topics and partitions to ensure fault-tolerant and scalable data streams, improving the reliability and performance of data flows across distributed systems.
Integrated Kafka with data processing frameworks such as Apache Spark and Databricks to enable real-time analytics and event-driven architectures.

Collaborated with cross-functional teams to design and maintain data lake architectures using Azure Data Lake and Databricks, ensuring data governance, security, and compliance across multiple environments.

Optimized data transformation logic using PySpark in Databricks to reduce data processing costs by leveraging Azure Auto Scaling capabilities, reducing compute costs by 20%.

Developed monitoring and alerting systems using Azure Monitor and Log Analytics for proactive identification of data pipeline failures, reducing downtime by 15% through faster incident resolution.

Managed and orchestrated data workflows using Azure Data Factory, ensuring smooth integration of multiple data sources such as SQL Server, Blob Storage, and REST APIs.

Performed data validation and quality checks using PySpark scripts in Azure Databricks, ensuring high accuracy and consistency in data processing and reporting pipelines.

Designed and implemented scalable data pipelinesusing Azure Data Factory and Azure Databricks (Python, PySpark), optimizing ETL processes to handle large datasets from diverse sources, improving data processing times by 30%.

Developed and automated end-to-end data workflowsleveraging Azure Data Factory, enabling seamless data ingestion, transformation, and storage in Azure Data Lake, ensuring 99.9% availability and reliability.

Built complex ETL solutions using PySpark in Azure Databricks, improving data aggregation and transformation performance, leading to a 40% increase in processing efficiency.

Migration efforts for on-premises data pipelines to Azure cloud environment, utilizing Azure Data Factory and Databricks, reducing infrastructure costs by 25%.

Implemented CI/CD pipelines in Azure DevOps for continuous integration and deployment of Azure Databricks notebooks and data pipeline code, reducing deployment time by 50% and improving code quality with automated testing.

Collaborated with cross-functional teams to design and maintain data lake architectures using Azure Data Lake and Databricks, ensuring data governance, security, and compliance across multiple environments.

Optimized data transformation logic using PySpark in Databricks to reduce data processing costs by leveraging Azure Auto Scaling capabilities, reducing compute costs by 20%.

Developed monitoring and alerting systems using Azure Monitor and Log Analytics for proactive identification of data pipeline failures, reducing downtime by 15% through faster incident resolution.

Managed and orchestrated data workflows using Azure Data Factory, ensuring smooth integration of multiple data sources such as SQL Server, Blob Storage, and REST APIs.

Performed data validation and quality checks using PySpark scripts in Azure Databricks, ensuring high accuracy and consistency in data processing and reporting pipelines.

Developed, maintained and automated and scalable ETL/ELT data pipelines

Sourced, processed, validated, transformed, aggregated, and distributed data from 10+ sources

Optimized and automated non-performant Database queries, complex processes, manual activities, and pipelines in 2019

Ensured timely access to data by 100% of applications

Developed monitoring and alerting capabilities to ensure 100% of data pipelines were working.

Developed and maintained ETL pipelines using Ab Initio, designing and implementing complex data integration processes to extract, transform, and load data from various sources, ensuring data accuracy and consistency.

Utilized Tivoli Workload Scheduler (TWS) to manage and automate job scheduling, optimizing workload management and ensuring timely execution of batch processes and data workflows.

Automated manual tasks through Unix scripting, creating shell scripts to streamline data processing, system monitoring, and routine maintenance tasks, resulting in increased operational efficiency and reduced manual errors.

Collaborated with stakeholders to gather requirements and translate them into technical specifications, ensuring alignment between business needs and technical solutions.

Implemented performance tuning and optimization strategies for ETL processes and scheduling jobs, improving system performance and reducing processing times.

Developed and maintained ETL pipelines using Ab Initio, designing and implementing complex data integration processes to extract, transform, and load data from various sources, ensuring data accuracy and consistency.

Utilized Tivoli Workload Scheduler (TWS) to manage and automate job scheduling, optimizing workload management and ensuring timely execution of batch processes and data workflows.

Automated manual tasks through Unix scripting, creating shell scripts to streamline data processing, system monitoring, and routine maintenance tasks, resulting in increased operational efficiency and reduced manual errors.

Collaborated with stakeholders to gather requirements and translate them into technical specifications, ensuring alignment between business needs and technical solutions.

Implemented performance tuning and optimization strategies for ETL processes and scheduling jobs, improving system performance and reducing processing times.

Developed and maintained Datat pipelines using Teradata and Unix scrips, designing and implementing complex data integration processes to extract, transform, and load data from various sources, ensuring data accuracy and consistency.

Utilized Tivoli Workload Scheduler (TWS) to manage and automate job scheduling, optimizing workload management and ensuring timely execution of batch processes and data workflows.

Collaborated with stakeholders to gather requirements and translate them into technical specifications, ensuring alignment between business needs and technical solutions.

Implemented performance tuning and optimization strategies for ETL processes and scheduling jobs, improving system performance and reducing processing times.