PepsiCo’s data platform drives insights across finance, marketing and data science. Delivering stability, scalability and developer delight is central to its success, and engineering leadership plays a key role in making this possible.

In this episode, Kunal Bhattacharya, Senior Manager of Data Platform Engineering at PepsiCo, shares how his team manages Airflow at scale while ensuring security, performance and cost efficiency.

Key Takeaways:

00:00 Introduction.

02:31 Enabling developer delight by extending platform capabilities.

03:56 Role of Snowflake, dbt and Airflow in PepsiCo’s data stack.

06:10 Local developer environments built using official Airflow Helm charts.

07:13 Pre-staging and PR environments as testing playgrounds.

08:08 Automating labeling and resource allocation via DAG factories.

12:16 Cost optimization through pod labeling and Datadog insights.

14:01 Isolating dbt engines to improve performance across teams.

16:12 Wishlist for Airflow 3: Improved role-based grants and database modeling.

Resources Mentioned:

Kunal Bhattacharya

https://www.linkedin.com/in/kunaljubce/

PepsiCo | LinkedIn

https://www.linkedin.com/company/pepsico/

PepsiCo | Website

https://www.pepsico.com

Apache Airflow

https://airflow.apache.org/

Snowflake

https://www.snowflake.com

dbt

https://www.getdbt.com

Kubernetes

https://kubernetes.io

Great Expectations

https://greatexpectations.io

Monte Carlo

https://www.montecarlodata.com

Thanks for listening to “The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.

#AI #Automation #Airflow #MachineLearning