Exploring The Expanding Landscape Of Data Professions with Josh Benamram of Databand

Summary "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. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. 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 Interview Introduction 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? Contact Info Email Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements 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. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Databand Website Platform Open Core More data engineering stories & best practices Atlassian Chartio Data Mesh Article Podcast Episode Grafana Metabase Superset Podcast.__init__ Episode Snowflake Podcast Episode Spark Airflow Podcast.__init__ Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast