“Business as usual” is changing, with more companies investing in data as a first class concern. As a result, the data team is growing and introducing more specialized roles. In this episode Josh Benamram, CEO and co-founder of Databand, describes the motivations for these emerging roles, how these positions affect the team dynamics, and the types of visibility that they need into the data platform to do their jobs effectively. He also talks about how his experience working with these teams informs his work at Databand. If you are wondering how to apply your talents and interests to working with data then this episode is a must listen.
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Josh Benamram about the continued evolution of roles and responsibilities in data teams and their varied requirements for visibility into the data stack
How did you get involved in the area of data management?
Can you start by discussing the set of roles that you see in a majority of data teams?
What new roles do you see emerging, and what are the motivating factors?
Which of the more established positions are fracturing or merging to create these new responsibilities?
What are the contexts in which you are seeing these role definitions used? (e.g. small teams, large orgs, etc.)
How do the increased granularity/specialization of responsibilities across data teams change the ways that data and platform architects need to think about technology investment?
What are the organizational impacts of these new types of data work?
How do these shifts in role definition change the ways that the individuals in the position interact with the data platform?
What are the types of questions that practitioners in different roles are asking of the data that they are working with? (e.g. what is the lineage of this asset vs. what is the distribution of values in this column, etc.)
How can metrics and observability data about pipelines and data systems help to support these various roles?
What are the different ways of measuring data quality for the needs of these roles?
How is the work you are doing at Databand informed by these changing needs?
One of the big challenges caused by data systems is the varying modes of access and interaction across the different stakeholders and activities. How can data platform teams and vendors help to surface useful metrics and information across these various interfaces without forcing users into a new or unfamiliar workflow?
What are some of the long-term impacts that you foresee in the data ecosystem and ways of interacting with data as a result of the current trend toward more specialized tasks?
As a vendor working to provide useful context to these practitioners what are some of the most interesting, unexpected, or challenging lessons that you have learned?
What do you have planned for the future of Databand?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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