Ever since I started writing about data product and data product management, I’ve had great discussions about exactly how to find a data product manager.
t’s always refreshing to see diverse perspectives and discussions centered around data products. Your insights on the evolving landscape of data are spot-on and incredibly valuable.
I particularly appreciated your points on making data products more accessible and practical for end-users, which aligns well with what we’re striving to achieve with dlt. As highlighted in our blog, our goal is to empower Python practitioners to autonomously handle data within their organizations, making the creation and maintenance of data pipelines simple and efficient. Your article really shines a light on the importance of this mission and the steps needed to make data more universally approachable.
Looking forward to more engaging content from your side. Keep up the amazing work!
Hey Eric! Great iteration. Interesting to learn more about data product in general, not that familiar with the field, and data and product are often times quite separated. I am new to your newsletter but have seen many of your posts on LinkedIn. Would love to have you on my podcast if you'd like to talk! :)
1. Could you explain what product sense is in regards to data for a data product manager? What are the signals and what are some muscles that potential candidates can work to strengthen?
2. There is also a pay disparity in engineering of what a data scientist would make vs a data product manager? what are some career pathways that can be established for data product managers to reach seniority in decision making and pay?
What a wonderful read! As someone deeply embedded in the mission and goals outlined in :
https://dlthub.com/blog/dlthub-mission:
t’s always refreshing to see diverse perspectives and discussions centered around data products. Your insights on the evolving landscape of data are spot-on and incredibly valuable.
I particularly appreciated your points on making data products more accessible and practical for end-users, which aligns well with what we’re striving to achieve with dlt. As highlighted in our blog, our goal is to empower Python practitioners to autonomously handle data within their organizations, making the creation and maintenance of data pipelines simple and efficient. Your article really shines a light on the importance of this mission and the steps needed to make data more universally approachable.
Looking forward to more engaging content from your side. Keep up the amazing work!
Best,
Aman Gupta,
DLT Team
Hey Eric! Great iteration. Interesting to learn more about data product in general, not that familiar with the field, and data and product are often times quite separated. I am new to your newsletter but have seen many of your posts on LinkedIn. Would love to have you on my podcast if you'd like to talk! :)
Excellent pointers, wonderfully written.
Here are my followup questions -
1. Could you explain what product sense is in regards to data for a data product manager? What are the signals and what are some muscles that potential candidates can work to strengthen?
2. There is also a pay disparity in engineering of what a data scientist would make vs a data product manager? what are some career pathways that can be established for data product managers to reach seniority in decision making and pay?