PRITHVI NAG KOLLA

Prithvi has a Master's in Data Science from the USA complemented by a Bachelor's in Computer Science from India, He brings a robust academic foundation to his role as a Data Science Leader with a global perspective. Over 5 years of hands-on experience, Prithvi has spearheaded the development, deployment, and management of data applications. His expertise spans the entire data lifecycle, from extraction and transformation to insightful analysis and visualization. With a proven track record in meticulous data validation, He ensures the accuracy and integrity of critical information and is Passionate about leveraging data-driven insights, Prithvi drive's strategic decision-making and foster business growth through innovative approaches. 

Work Experience

Machine Learning Engineer

2024 - Present

•  Developing and managing POC applications that serves as a proof of concept for data solutions at scale. It's a hands-on project where I get to explore new approaches and technologies, and it’s something I get to present to stakeholders to validate our ideas.

• Designing and implementing robust data pipelines that handle large amounts of data efficiently. These pipelines are built to process, transform, and move data across various systems, so they need to be reliable and scalable.

• Integrating data from various sources into our systems, ensuring it's accurate and consistent. This requires understanding the structure of the data and ensuring it's clean and ready for analysis.

• Working with services like AWS S3 for storing data, Redshift for data warehousing, and EC2 for running our compute tasks. This allows us to scale up or down based on our needs and manage data effectively in the cloud.

• GitHub Actions to automate much of the work. This means I can set up continuous integration and delivery pipelines to automatically test, build, and deploy code. It’s key for speeding up development and reducing manual errors.

• One of my main tasks is to optimize the performance of our data processing workflows. Whether it’s improving how fast data is ingested or reducing costs related to storage or compute, I’m always looking for ways to make our systems more efficient.

• I work with data engineers, managers, data scientists, analysts, and product teams to understand their requirements and translate those into technical solutions. Whether it’s providing them with clean data for analysis or creating dashboards, we collaborate closely to ensure the final product meets everyone’s needs.

• Given the sensitivity of data, ensuring our solutions are secure and compliant with regulations is critical. I follow best practices for data governance, so we make sure the data is protected, and the organization stays compliant. 

Data Engineer

2021 - 2024

• Spearheaded black box testing initiatives on the AWS data processing framework, ensuring robust data quality and reliability.

• Identified errors and meticulously documented bug issues in JIRA for resolution, streamlining the debugging process.

• Closed bug issues after thorough retesting to ensure alignment with business requirements and data integrity.

• Championed test automation by devising solutions in PySpark, automating redundant test cases within regression testing on vast datasets, significantly improving data quality and testing efficiency.

• Significantly reduced manual testing efforts and enhanced performance by expediting test report delivery using PySpark automation techniques.

• Leveraged GitHub workflow actions, scripted YAML Workflow, and automated regression tests with PySpark, mitigating issues and substantially improving performance and deployment processes.

• Meticulously created test scenarios, test cases, and test data for development features, ensuring comprehensive test coverage and data accuracy.

• Documented and audited test reports during daily sprints and on release days, maintaining transparency and accountability in testing processes.

• Managed offshore team members by assigning tasks through JIRA and providing pre-sprint presentations, ensuring seamless implementation of new processes and effective collaboration.

• Donned multiple hats – from Developer and Tester to DevSecOps, leveraging tools such as JIRA, GitHub, Python, and PySpark to drive efficiency and innovation in data engineering projects.

• Achieved operational excellence and drove test automation solutions that aligned with company standards, enhancing overall data processing and quality assurance frameworks using PySpark.