Data

Backend Data

Building Faster Indexing with Apache Kafka and Elasticsearch

DoorDash describes how it built a faster search index using open source projects.

Satish Saley Danial Asif Siddharth Kumar
Data

Overcoming Rapid Growth Challenges for Datasets in Snowflake

To meet additional SLA's of DoorDash's rapidly growing team without increasing compute we had to rely on a variety of ETL optimizations.

Andrew Huynh Ashwini Manjunath
Data Machine Learning

Building Riviera: A Declarative Real-Time Feature Engineering Framework

In a business with fluid dynamics between customers, drivers, and merchants, real-time data helps make crucial decisions which grow our business and delights our customers. Machine learning (ML) models play a big role in improving the experience on our platform, but models can only be as powerful as their underlying features. As a result, building ...

Allen Wang Kunal Shah
Data Machine Learning

How to Drive Effective Data Science Communication with Cross-Functional Teams

Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many ...

James Williams Lokesh Bisht
Data Machine Learning

Running Experiments with Google Adwords for Campaign Optimization

Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...

Yingying Chen Heming Chen
Data Machine Learning

Building Flexible Ensemble ML Models with a Computational Graph

DoorDash extended its machine learning platform to support ensemble models.

Hebo Yang Arbaz Khan Param Reddy
Backend Culture Data General Machine Learning Mobile Web

2020 Hindsight: Building Reliability and Innovating at DoorDash

DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.

WayneCunningham
Data

The Undervalued Skills Candidates Need to Succeed in Data Science Interviews

After interviewing over a thousand candidates for Data Science roles at DoorDash and only hiring a very small fraction, I have come to realize that any interview process is far from perfect, but there are often strategies to improve one’s chances . Over the course of our interviews, I’ve come across some great candidates who ...

Lokesh Bisht
Data Machine Learning

Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible ...

Arbaz Khan Zohaib Sibte Hassan
Data Machine Learning

Uncovering Online Delivery Menu Best Practices with Machine Learning

Learn how we analyzed over 100K online delivery menus to develop menu best practices

Finn Qiao
Data

Hot Swapping Production Tables for Safe Database Backfills

DoorDash engineering explains how to edit large data tables safely and quickly in a production database.

Justin Lee
Data Machine Learning

Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivity

To speed up the development of new features we needed a way to increase our experiment capacity. Learn how we improved it by 4X

Jessica Zhang Yixin Tang

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