5 women pioneering Data Infrastructure Engineering at Uber

Uber’s Data Infrastructure Engineering team democratizes fast, efficient, and reliable data products across the company to help us unlock business insights and make informed decisions. Meet 5 women driving Uber’s data infrastructure development and realizing our vision of moving the world with global data, local insights, and intelligent decisions.

An Sheng
Engineering Manager, Business Intelligence Tools, Querying Platform

What’s your role? 

“I manage the Querying Platform that powers Uber’s Business Intelligence Tools. We build the next generation of analytics tools to guide business decisions at Uber. Our users cover a broad range of roles: operations teams, data scientists, product managers, engineers, and even executive leadership. We provide teams all across Uber with an entry point to data, giving them quick and reliable options to explore, build and share.” 

What excites you about the work you do?

“The opportunity to make a huge impact at the ground level. We’re scaling our services to match Uber’s analytical needs, which is a challenging problem and also super rewarding. We are the engine – quite literally the query engine – that allows this company to make decisions, big or small. Teams on the ground need to visualize their city health and understand customer issues. Product teams need to measure the success of their features. Leadership needs insight into initiatives that support the vision of Uber. Being able to provide tools for the company that have such a wide reach is super exciting.”

What was your recruiting process like?

“We look for candidates who are passionate about what they do. Who have expertise in areas they’ve worked in and have an eagerness to learn in areas they haven’t. I like to speak with candidates early to understand their interests and help align them with the team’s goals. At the end of the day, working on something you love will establish a deeper sense of ownership and make your day-to-day much more enjoyable.”

Jing Shi
Engineering Manager, Data Workflow Platform

What’s your role? 

“I manage the data workflow platform team to build a scalable, reliable and multi-datacenter platform that orchestrates, schedules and executes production-grade batch jobs and unified UI to author and manage batch workflows. I also support our vision of connecting Uber’s business-critical use cases of across Ridesharing, Safety, and Marketplace to data services that provide end-to-end workflow solutions.”

What are the most interesting challenges you need to solve?

“I always like to learn new technologies, as well as engage with peers to solve system architecture and business problems. Our data workflow platform is used by thousands of Data Scientists, Business Analysts, AI/ML and Data Engineers across Uber daily and continuously to improve our distributed services reliability, scalability and performance––which is very challenging. On the other hand, we also build the platform for both tech savvy and non-tech savvy users. It requires us to deeply understand business use cases and develop a great product.”

How do you ensure you continue to grow?

“Reading, stepping out of my comfort zone, keeping curiosity and setting goals for personal growth.”

Ujwala Tulshigiri
Engineering Manager, Data Real Time Analytics

What’s your role? 

“I manage our real-time analytics platform to power business-critical use cases (such as trip flows, order, maps ETAs) and real-time dashboards across Uber’s lines of business, including: Uber Eats, Uber Freight, Driver, Customer Obsession, and our Ads Platform. The need for real-time analytics continues to grow rapidly within Uber. In order to meet growing demand, we provide a fast, reliable, and scalable analytics solution that enables users with real-time insights into their data without worrying about underlying complexities. This fully self-service platform is based on Apache Pinot and Presto for low-latency SQL analytics.”

Why did you join Uber? 

“After working on enterprise storage systems for more than 8 years, I wanted to explore the new and exciting space of real-time analytics. I think Uber is perfectly positioned to provide such opportunities because, here, we have to process billions of messages per day and extract crucial insights with sub-second latencies impacting key business decisions.”

What would you tell someone interested in joining your team?

“If you are excited to work on the cutting edge of real-time analytics systems and understand the unique challenges that come with operating on a massive scale of data, this is the right team for you! There are plenty of opportunities for open-source contributions to Apache Pinot and Presto, collaborating with Uber’s team and making a significant impact on our success.”

Jing Li
Senior Software Engineer, Data Ingestion and Monitoring 

What’s your role? 

“I’m a Software Engineer on the Data Ingestion team. We provide reliable transportation of high-quality data from various sources into our Hadoop data lake. The raw datasets ingested by our platform are the source of truth for all batch data at Uber. Users from all organizations rely on our platform to provide powerful data analysis and insights including compliance, finance reports, ETA predictions, Uber Eats recommendations, demand forecasting, and service operational metrics.” 

Why did you join Uber? 

“It’s an exciting opportunity to expand my knowledge beyond application and web service development. The vast amount of data Uber processes makes the opportunity even more attractive as I’m not only entering an entirely new domain, but also have a ton of challenges waiting for me to explore and conquer.”

What would you tell someone interested in joining your team?

“If you’re looking for projects with massive scale and a game-changing opportunity to improve dataset freshness, this is the team you need to be part of. Our data ingestion platform powers users across Uber and interacts with technologies and teams to drive our success.”

Yang Yang
Senior Software Engineer, Kafka Team

What’s your role? 

“I’m a Software Engineer on the Kafka team, primarily focusing on building a highly scalable, reliable streaming ecosystem running across multiple data centers. Uber has one of the largest deployments of Apache Kafka in the world, processing trillions of messages and multiple petabytes of data per day. Kafka is the cornerstone of Uber’s technology stack. It empowers Uber’s business. It passes event data from the Rider and Driver applications, enabling a streaming analytics platform streaming database change logs to downstream subscribers, and ingesting all sorts of data into Uber’s Apache Hadoop data lake.”

What’s your typical day like?

“The day usually begins by going over the email, pending code reviews from my teammates. Then I check the calendar and plan. Plans vary day-to-day, but most of my time is spent writing code and discussing project design with my team.”

If you’re interested in joining us, explore open roles on our team here, here, and here, and other Uber Engineering roles ->

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