Atlan > Case Studies > Scaling Postman's Data Team: A Case Study on Rapid Growth and Process Improvement

Scaling Postman's Data Team: A Case Study on Rapid Growth and Process Improvement

Atlan Logo
Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries
  • Buildings
  • Construction & Infrastructure
Applicable Functions
  • Sales & Marketing
Use Cases
  • Construction Management
  • Time Sensitive Networking
Services
  • Data Science Services
  • System Integration
About The Customer
Postman is an API collaboration platform that aims to simplify and expedite API processes. The company, which started as a side project six years ago, has grown to become one of India's latest unicorns with a valuation of $5.6 billion. Postman's platform is used by over 17 million people from 500,000 companies globally. The company has a 400-member team spread across two offices in Bangalore, one in San Francisco, and distributed remotely across four continents. The data team, which is based in Bangalore, consists of 25 members divided into two sub-teams: the Data Engineering Team and the Data Science Team.
The Challenge
Postman, an API collaboration platform, experienced rapid growth, with its valuation reaching $5.6 billion and its user base expanding to over 17 million people from 500,000 companies globally. However, the company's data team was not growing at the same pace. In April 2020, the data team consisted of only six or seven people. Over the next year, the team expanded by 4-5x to 25 people. This rapid growth presented challenges in terms of onboarding new hires, handling requests from the rest of the company, and planning their work. The data team was also grappling with the decision between a centralized and decentralized team structure, with the former leading to conflicting data systems and metrics. Additionally, the team faced difficulties in prioritizing work and allocating projects fairly among team members.
The Solution
To address these challenges, Postman's data team implemented several strategies. They moved towards a more centralized team structure, hiring analysts into the Data Science Team and training them centrally. After a several month onboarding process, some new hires would move to more data-mature teams as embedded or decentralized analysts. The team also introduced a hierarchy to distinguish between different levels of data analysts. To manage work prioritization, they implemented a ticketing system on Jira, where anyone in the organization could create a ticket detailing their data request. The team would then follow up on each ticket, gathering more information and assigning each ticket to a sprint based on its priority and impact. To allocate projects, the team introduced weekly Issue Grooming sessions, where they would assess and assign new tasks. The team also adopted the sprint methodology, breaking down projects into smaller tasks and conducting sprint retrospectives to continuously improve their processes.
Operational Impact
  • The implementation of new processes and structures significantly improved the efficiency and effectiveness of Postman's data team. The move towards a more centralized team structure eliminated the issue of conflicting data systems and metrics. The introduction of a hierarchy provided clarity on roles and responsibilities within the team. The ticketing system on Jira and the weekly Issue Grooming sessions helped in managing work prioritization and project allocation, ensuring that tasks were distributed fairly and based on their impact and priority. The adoption of the sprint methodology encouraged the team to break down projects into smaller tasks, promoting continuous output and enabling the team to identify and address issues in a timely manner. These improvements have made the data team more comfortable with onboarding new hires, handling requests from the rest of the company, and planning their work.
Quantitative Benefit
  • Postman's data team grew by 4-5x to 25 people in just over a year.
  • The company's valuation reached $5.6 billion, with its user base expanding to over 17 million people from 500,000 companies globally.
  • Nearly one quarter of the company is active on Looker every week, using the data processed by the Data Science Team.

Case Study missing?

Start adding your own!

Register with your work email and create a new case study profile for your business.

Add New Record

Related Case Studies.

Contact us

Let's talk!
* Required
* Required
* Required
* Invalid email address
By submitting this form, you agree that IoT ONE may contact you with insights and marketing messaging.
No thanks, I don't want to receive any marketing emails from IoT ONE.
Submit

Thank you for your message!
We will contact you soon.