About this episode
Summary
Your ability to build and maintain a software project is tempered by the strength of the team that you are working with. If you are in a position of leadership, then you are responsible for the growth and maintenance of that team. In this episode Jigar Desai, currently the SVP of engineering at Sisu Data, shares his experience as an engineering leader over the past several years and the useful insights he has gained into how to build effective engineering teams.
Announcements
- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!
- When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode 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!
- The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.
- Your host as usual is Tobias Macey and today I’m interviewing Jigar Desai about building effective engineering teams
Interview
- Introductions
- How did you get introduced to Python?
- What have you found to be the central challenges involved in building an effective engineering team?
- What are the measures that you use to determine what "effective" means for a given team?
- how to establish mutual trust in an engineering team
- challenges introduced at different levels of team size/organizational complexity
- establishing and managing career ladders
- You have mostly worked in heavily tech-focused companies. How do industry verticals impact the ways that you think about formation and structure of engineering teams?
- What are some of the different roles that you might focus on hiring/team compositions in industries that aren’t purely software? (e.g. fintech, logistics, etc.)
- notable evolutions in engineering practices/paradigm shifts in the industry
- What are some of the predictions that you have about how the future of engineering will look?
- What impact do you think low-code/no-code solutions will have on the types of projects that code-first developers will be tasked with?
- What are the most interesting, innovative, or unexpected ways that you have seen organizational leaders address the work of building and scaling engineering capacity?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working in engineering leadership?
- What are the most informative mistakes that you would like to share?
- What are some resources and reference material that you recommend for anyone responsible for the success of their engineering teams?
Keep In Touch
Picks
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.
- 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 hosts@podcastinit.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec 2022
Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
The majority of machine learning projects that you read about or work on are built around batch processes. The model i ... Show More
1h 16m
Dec 2022
Declarative Machine Learning For High Performance Deep Learning Models With Predibase
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference ... Show More
59m 22s
Nov 2022
Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks
Preamble
This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Summary
Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the mo ... Show More
47m 37s
Feb 2025
#495: OSMnx: Python and OpenStreetMap
On this episode, I’m joined by Dr. Jeff Boeing, an assistant professor at the University of Southern California whose research spans urban planning, spatial analysis, and data science. We explore why OpenStreetMap is such a powerful source of global map data—and how Jeff’s Python ... Show More
1h 1m
Sep 2021
An Exploration Of The Data Engineering Requirements For Bioinformatics
Summary
Biology has been gaining a lot of attention in recent years, even before the pandemic. As an outgrowth of that popularity, a new field has grown up that pairs statistics and compuational analysis with scientific research, namely bioinformatics. This brings with it a uniqu ... Show More
55m 10s
May 2022
Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way
Summary
Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional layers of difficulty. Srivatsan Sridharan has had the ... Show More
58m 11s
Mar 2021
Data Quality Management For The Whole Team With Soda Data
Summary
Data quality is on the top of everyone’s mind recently, but getting it right is as challenging as ever. One of the contributing factors is the number of people who are involved in the process and the potential impact on the business if something goes wrong. In this episod ... Show More
58 m
Aug 2024
The Evolution of DataOps: Insights from DataKitchen's CEO
Summary
In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Chris Berg, CEO of DataKitchen, to discuss his ongoing mission to simplify the lives of data engineers. Chris explains the challenges faced by data engineers, such as constant system failures ... Show More
53m 30s
Feb 2025
The Future of Data Engineering: AI, LLMs, and Automation
Summary
In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large langua ... Show More
59m 39s
Feb 2024
Using Trino And Iceberg As The Foundation Of Your Data Lakehouse
Summary
A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. In this ... Show More
58m 46s
Aug 2018
258: A Foot in the Door
This week, we debut the new show format! First, Marshall formally introduces himself, and we answer a listener's question about how to get their foot in the UX door. Then we cover a few headlines, fight about stock vs. third-party apps, and share a couple cool things. If you have ... Show More
38m 51s
Aug 2019
Building Tools And Platforms For Data Analytics
Summary
Data engineers are responsible for building tools and platforms to power the workflows of other members of the business. Each group of users has their own set of requirements for the way that they access and interact with those platforms depending on the insights they are ... Show More
48m 7s
Dec 2024
The Art of Database Selection and Evolution
Summary
In this episode of the Data Engineering Podcast Sam Kleinman talks about the pivotal role of databases in software engineering. Sam shares his journey into the world of data and discusses the complexities of database selection, highlighting the trade-offs between differen ... Show More
59m 56s