Ashwin Singamsetty Email & Phone Number

United States

Charlotte, North Carolina, United States

• Designed and implemented scalable cloud-based data infrastructure using AWS services (S3, Redshift, Glue, Lambda), processing over 50 TB of data daily for critical business applications.
• Led migration from on-premises systems to AWS, leveraging Glue and Redshift to reduce operational costs by 40% and increase data processing speeds by 35%.
• Automated ETL workflows with Apache Airflow and AWS Glue, minimizing manual interventions by 80% and ensuring near real-time data availability.
• Developed real-time data streaming pipelines with Apache Kafka, enabling rapid insights and supporting time-sensitive business decisions.
• Managed and guided a team of 10 data engineers using JIRA and Confluence, improving team efficiency and reducing incident response times by 20%.
• Integrated machine learning models into data workflows using PySpark, accelerating model deployment by 50%.

Corte Madera, California, United States

• Designed, developed, and maintained scalable ETL data pipelines for both batch and streaming processes, effectively handling structured and unstructured data formats to meet diverse organizational needs.
• Utilized advanced analytics tools and methodologies to enhance the analytics platform, empowering data-driven decision-making across the organization.
• Collaborated closely with business and technical teams to gather requirements and design comprehensive data solutions aligned with key organizational objectives.
• Implemented complex data operations using Python, Java, Spark, and SQL, optimizing ETL/ELT processes for peak performance, reliability, and efficiency.
• Managed data across various platforms, leveraging cloud services and big data technologies to create scalable and flexible data solutions.
• Developed and maintained data models that supported critical business processes and enabled strategic decision-making.

Denver, Colorado, United States

• Built and optimized ETL workflows using Python and Apache Spark, processing large datasets from APIs, relational databases, and flat files.
• Improved SQL query performance in Microsoft SQL Server and Oracle, reducing data processing times by 30%.
• Designed and implemented data ingestion pipelines using Talend and Azure Data Factory, improving data availability for analytics teams.
• Assisted in migrating workloads to AWS, leading to a 20% improvement in system performance and reducing operational costs by 25%.
• Automated batch processing tasks with Apache Airflow, achieving 98% uptime for data pipelines and improving reliability.
• Developed data quality processes using AWS Glue DataBrew to ensure accuracy and timely delivery of insights.

New York, New York, United States

• Developed, deployed, and optimized scalable data solutions within cloud platforms such as AWS and Azure, enabling efficient data processing and access.
• Designed and maintained data pipelines to manage the ingestion, transformation, and processing of data using tools like Apache Spark, Apache NiFi, and Talend.
• Built and managed data lakes and data warehouses, facilitating the storage and retrieval of complex, large-scale datasets for analytics and business intelligence purposes.
• Utilized programming expertise in Python and SQL to automate data processes, enhance data transformation workflows, and improve overall data pipeline performance.
• Collaborated with cross-functional teams to deliver data solutions aligned with business needs, leveraging tools like JIRA, Confluence, and Slack for effective communication and project management.
• Implemented data governance and validation processes to ensure high data quality, accuracy, and compliance with Barclays' standards and industry regulations.

Hyderabad, Telangana, India

• Developed and maintained ETL processes using tools such as Informatica and IBM DataStage to extract, transform, and load data from various sources into data warehouses, ensuring data consistency and accuracy.
• Collaborated with data architects and senior engineers to understand project requirements and build data pipelines tailored to client specifications and business needs.
• Integrated data from multiple sources, including relational databases (Oracle, SQL Server) and flat files, to create unified datasets for analytics and decision-making.
• Created and optimized data models that aligned with client requirements, facilitating efficient data storage, retrieval, and reporting.
• Developed and executed SQL queries for data extraction, transformation, and loading, maintaining high standards of performance and data quality.
• Implemented data validation and quality control processes to ensure data accuracy, using tools like SQL scripts and validation frameworks.

Master'S Degree, Computational Data Science

Bachelor'S Degree, Computer Science