Saket Saurabh

His co-founder live-coded a fix during the Instacart pitch - and closed the deal on the spot. Saket Saurabh used consultative selling SaaS techniques to close 15 enterprise customers including Instacart, LinkedIn, and DoorDash before hiring a single salesperson. Saket reveals why he went "enterprise first" instead of starting with SMBs, the consultative selling SaaS approach that turns every meeting into problem-solving instead of pitching, and the zero-salary pivot that made Nexla cash flow positive before their $12M Series A. Nexla is an enterprise data platform serving 50+ customers with 6-figure ACV deals. Saket's founder-led sales motion grew the company to over $5M ARR after raising $33M total. This episode is brought to you by: 💖 Gearheart → Book a free consult and get the first 20 hours free 🚨 NordStellar → Book a demo and get 20% off with code blackfriday20 🔑 Key Lessons 🤝 Consultative selling SaaS connects product to market: Unless founders sell deals themselves, they miss critical signals about pricing and product direction. Saket closed 15 enterprise customers before hiring salespeople. 🪄 Create "magical moments" in demos: Saket's co-founder live-coded a data fix during the Instacart CTO pitch, solving in minutes what took their team weeks. Enterprise selling with agility closes deals faster than slides. 🏢 Go enterprise first to build for real complexity: Architecting for SMBs first prevents you from understanding enterprise-grade problems. Nexla targeted Fortune 500 companies from day one. 🎯 Use thesis-driven outreach instead of cold pitching: Saket built specific hypotheses about each target company's data problems. Starting with "Do you see this problem?" earned trust with technical buyers. 💰 Price against internal build cost, not competitors: Saket estimated what the prospect would spend on internal engineering, then priced Nexla at one-fifth to one-tenth. Consultative selling SaaS means understanding the buyer's economics. Chapters Introduction - the "magical moment" at Instacart What is Nexla? Solving enterprise data fragmentation Origin story: from Nvidia engineer to data entrepreneur Why target enterprise customers from day one What a typical consultative selling meeting looked like The live-coding demo that closed Instacart Figuring out enterprise pricing Closing 15 enterprise deals through founder-led sales Overcoming the "we can build it ourselves" objection The zero-salary pivot to cash flow positivity How AI changed Nexla's product and market Lightning round Resources Full show notes: https://saasclub.io/464 Join 5,000+ SaaS founders: https://saasclub.io/email

Saket Saurabh

Co-Founder and CEO

Nexla

Summary The technological and social ecosystem of data engineering and data management has been reaching a stage of maturity recently. As part of this stage in our collective journey the focus has been shifting toward operation and automation of the infrastructure and workflows that power our analytical workloads. It is an encouraging sign for the industry, but it is still a complex and challenging undertaking. In order to make this world of DataOps more accessible and manageable the team at Nexla has built a platform that decouples the logical unit of data from the underlying mechanisms so that you can focus on the problems that really matter to your business. In this episode Saket Saurabh (CEO) and Avinash Shahdadpuri (CTO) share the story behind the Nexla platform, discuss the technical underpinnings, and describe how their concept of a Nexset simplifies the work of building data products for sharing within and between organizations. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today 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! Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Saket Saurabh and Avinash Shahdadpuri about Nexla, a platform for powering data operations and sharing within and across businesses Interview Introduction How did you get involved in the area of data management? Can you describe what Nexla is and the story behind it? What are the major problems that Nexla is aiming to solve? What are the components of a data platform that Nexla might replace? What are the use cases and benefits of being able to publish data sets for use outside and across organizations? What are the different elements involved in implementing DataOps? How is the Nexla platform implemented? What have been the most comple engineering challenges? How has the architecture changed or evolved since you first began working on it? What are some of the assumptions that you had at the start which have been challenged or invalidated? What are some of the heuristics that you have found most useful in generating logical units of data in an automated fashion? Once a Nexset has been created, what are some of the ways that they can be used or further processed? What are the attributes of a Nexset? (e.g. access control policies, lineage, etc.) How do you handle storage and sharing of a Nexset? What are some of your grand hopes and ambitions for the Nexla platform and the potential for data exchanges? What are the most interesting, innovative, or unexpected ways that you have seen Nexla used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Nexla? When is Nexla the wrong choice? What do you have planned for the future of Nexla? Contact Info Saket LinkedIn @saketsaurabh on Twitter Avinash LinkedIn @avinashpuri on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Nexla Nexsets The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Saket Saurabh

Co-Founder and CEO

Nexla