Aspiring data engineers? with Mohan Ganesan at AAA
The Data Standard
Episode Summary
Data Engineering is an interdisciplinary profession requiring a mix of technical and business knowledge to have the most impact. Starting a career in data engineering, it is not always clear what is necessary to be successful. Some people believe that there is a need to learn particular technologies (e.g., Big data); others believe it is a high degree of software engineering expertise; others believe it is focusing on business. There are five main tips this podcast would give to data engineers starting their careers: Learn fast Dont succumb to the technology hype Data Engineering is not all about coding or technology or data Coding is still an important part of the job Know your data and where it is coming from
Meet The Host
Catherine Tao
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
Mohan Ganesan
Senior Software Engineer, Data & Analytics at AAA
Hello! I am a data engineer with extensive work experience on pipeline integrations across a multitude of industry verticals including portfolio management, insurance, retail, and energy. I am able to bring to the table a unique combination of a strong technical background in software engineering coupled with keen analytical skills and the ability to quickly acquire business domain knowledge. I always strive to serve as a formidable link between business and engineering, being able to communicate effectively with stakeholders on business expectations and requirements and subsequently wear my technical hat to turn business logic requirements into quality data platforms for effective reporting and analytics. I thoroughly enjoy the process of developing data pipelines that through extensive pre-processing and cleansing are able to derive actionable insights from a variety of raw data sources, and have a proven track record of driving substantial business value for end users through these insights.
