Thanks Shane. Welcome everyone to our third quarter earnings call. I'm pleased to say that Confluent delivered another strong quarter with the results exceeding the high end of all our guided metrics. Total revenue grew 48% year-over-year to $152 million. Confluent Cloud continued to be the fastest-growing area of our business with revenue up 112% year-over-year to $57 million. We are also continuing to increase our operating leverage with a 14-point year-over-year improvement in non-GAAP operating margins. Despite the pressure from a more difficult macroeconomic backdrop, we think these strong and consistent results are a testament to our ability to drive durable and efficient growth. The rise of data streaming is one of the most fundamental shifts in the world of data. So, it will be hard to imagine a time when companies didn't have ubiquitous access to real-time data and the ability to react to it instantaneously. Apache Kafka has emerged as the de facto standard of this movement, hundreds of thousands of organizations including more than 75% of the Fortune 500 use it every day for critical use cases across their business. But there are also incredible things happening within the larger data streaming ecosystem, including an extraordinary number of new use cases and technologies. In October, we hosted Current 2022, the first ever industry event for data streaming. It put Confluent at the center of the ecosystem and brought together over 4,000 attendees, hundreds of ecosystem partners, and more than 50 sponsors to learn, network, and explore the future of data streaming. In today's call, I want to give a view of this emerging data streaming category and explain how it relates to some of the legacy technologies it replaces. We believe data streaming represents a major new data platform that has the potential to be as broad in scope as databases have been. However, many of the legacy products in this space have been quite limited in adoption and scope. How can we square the skyrocketing adoption of Kafka rapid success of Confluent and expansive view most technologists have about streaming with more limited success previous tools in the space have had? To answer that requires a brief excursion through the legacy technologies that Confluent displaces. Today, we can broadly think of three major states of software and data. First, custom applications the business is built from scratch and the operational databases that support them. Second, the hundreds of SaaS applications like Salesforce and Workday that address common yet critical needs for business functions. And finally, analytics systems improve decision-making. Each of these areas has grown a set of ad hoc fragmented tools for integration and improved kind of data in motion. Let's review these previous generation of tools and then discuss how it is displaced by data streaming. Custom applications communicate with message queues, database change capture products, ad hoc APIs, and enterprise service buses. These technologies fit the real-time requirements of application workloads, but were impossible to scale low level and labor-intensive to work with and limited in their application. SaaS applications meanwhile grew their own set of tools including proprietary application integration platforms, business process, management tools, and bulk file transfers. These tools achieved some success in their domains, but were again limited in scalability unable to handle complex data transformations or logic, fragile to work with, and disconnected from modern development platforms. Finally, businesses drive data into analytical systems, primarily through a combination of legacy ETL or ELT tools as well as preprocessing and data lakes. These tools support rich transformations but are stuck with slow batch processing that makes the data hours or days late by the time it arrives. These tools are all flawed in different ways. They're either slow and batch oriented, nonscalable, requires significant maintenance from centralized teams or are unable to work with more sophisticated data and processing. But more importantly, the critical problem is that in a modern company, all three of the major estates of data must be highly connected. The custom applications must interconnect with the off-the-shelf applications, and the analytics applications. Consider a simple example of a modern retailer. Data about what is selling is needed by dozens or hundreds of custom applications, SaaS applications and analytics platforms having to create point-to-point plumbing across a dozen different tools, for each use case and simply not feasible or scalable approach. Data streaming works by rethinking this problem from the ground up, whereas the previous generation of tools were ad hoc and limited to a narrow domain. Data streaming starts with a broad foundational approach. It takes the core architectural concepts of a database such as, ledger of changes, transactional guarantees, horizontal scalability and easy dynamic data transformations and translates them from the world of data at rest, to data in motion. We believe the result is something vastly more general and powerful, than any of the previous solutions. It consolidates the fragmented ecosystem of integration tools with a solution, that can achieve all the capabilities, each of the previous tools could not. It's real time, it's horizontally scalable, it's transactionally correct and it provides an open programmable platform. This provides a solution, that is better in each of the domains than the previous generation of systems, but its key strength is that it treats data in a reusable manner. A single stream of data can feed all use cases, whether custom applications, SaaS applications or analytic stores. This allows vastly more simplicity and reuse than previous solutions, but the power of data streaming is well beyond the integration technologies of yesterday, because it starts with an open programmable foundation, is not limited to building pipelines. The stream processing capabilities in Kafka, allow any application logic whether for data transformation or applying smart business rules. This is what has made Confluent, a foundation for developing real-time applications that react to the stream of business events continuously. The real-time applications that organizations can build with Confluent, are limitless includes fraud detection, fleet management, customer 360 platforms, real-time inventory management and many more. Thus, while each of the legacy technologies was limited in scope and adoption, data streaming has much broader potential as it both consolidates this landscape and expands well beyond it. Indeed, considering the three estates of data I mentioned before, the custom applications, SaaS applications and analytics systems. It's worth noting, that each of these is a repository of stored data. That is, data at rest. However, equally important, is the data in motion that Confluent is providing the underlying platform and foundation for. We believe this data in motion represents a fourth estate of data, which will be equally critical to the operation of a modern business. This background provides a good framework for contextualizing, a few exciting product releases from Confluent. While Confluent has revolutionized the underlying infrastructure for integration with data streaming, some of the legacy tools still had one advantage. Whereas Confluent was primarily a programmatic tool, many of the legacy tools were low code or no code guise, which while limited in power were easy to learn and use. This is what makes me so excited, for our recent announcement of Stream Designer. Stream Designer brings a dead simple UI for building pipelines, familiar from other integration tools, but it does it on top of our modern data streaming platform. Stream Designer is the first drag-and-drop, visual builder to rapidly build and deploy streaming pipelines, natively on Kafka. It integrates with the core capabilities of our platform, Kafka Connect, Stream Processing and Governance to make building mission-critical data pipeline simple. Stream Designer also makes deploying streaming data pipelines accessible to more people throughout an organization, including people less familiar with Kafka. Users with different skill sets, don't have to give up the power of the underlying infrastructure either. They can seamlessly switch between the UI, code editor and command-line interface to quickly and declaratively build data pipelines. Back in 2021, we mentioned a wide variety of up-the-stack use cases, we are uniquely positioned to address. Stream Designer represents our first step in that direction, and lets us serve the set of use cases broadly characterized as data pipelines. This isn't the end of the story though. By having one layer where data flows throughout the organization, this enables Confluent to add additional value. As data streaming use cases grow, and real-time data flows more freely across the business, it's critical that this data can easily be discovered, understood and governed in real time. Stream Governance Advanced does exactly that. The newest capability in our Stream Governance suite makes governing mission-critical workloads at any scale more reliable, with a 99.95% uptime. And the ability to add user-generated business context, makes it easier to find the data that's needed to power new use cases. Now customers can more easily scale the power of data streaming, from individual projects to central nervous systems for their business. Taken together Stream Designer and Stream governance advanced are powerful examples of the fundamental paradigm shift occurring with data streaming. They also show our unique ability to build out-of-the-box solutions on top of our platform that reach a broader set of customers, accelerate their adoption and grow our addressable market over time. It was once thought of as different software categories are today consolidating into one much more general powerful and valuable market data streaming platforms. Next I'd like to touch on something I mentioned in my opening remarks, Confluent is set up to drive durable and efficient growth. We've seen fantastic momentum to date but what's most exciting is our approximately $60 billion market opportunity in front of us. We've started to demonstrate both the breadth and depth of this opportunity. The breadth is captured by the massive adoption of Kafka, which provides a large installed base for new customer acquisition through self-service on Confluent Cloud. This is foundational to our strategy of converting open source users and landing greenfield customers in high volume. We've also shown the depth of the value of these opportunities with our ability to expand rapidly after we land a customer. This is evident in our best-in-class large customer ratio where 22% of our customers have an ARR of $100,000 or more. But at the same time we believe there are strong expansion opportunities with our largest customers still including those with ARR of $10 million or more, particularly as we make it easier to connect and consume data throughout the platform. We also continue to benefit from the secular move to the public cloud, particularly in an environment where there's increased pressure for organizations to run their businesses more efficiently. Our cloud-native platform significantly simplifies operational complexities and reduces total cost of ownership saving valuable engineering resources allocated to manually building and managing lower-level infrastructure tools like Kafka. A new Q3 customer is a great example of the cost savings of Confluent Cloud. Armis is a leading cybersecurity platform for connected devices that enables its customers to discover and secure their IT, cloud, IoT and edge assets in real time. Today Armis tracks over 3 billion devices for its customers from printers, laptops and mobile devices to connected medical devices and factory equipment. Kafka is a central part of their business responsible for ingesting data bidirectionally from billions of devices to provide real-time protection and policy enforcement. But with the rapid growth of its business and the proliferation of connected devices the cost and overhead of managing open source Kafka was unwieldy This quarter Armis turned to Confluent Cloud for a cloud-native Kafka service that can scale alongside its business. Confluent will be the central nervous system for Armis' data streaming platform managing data from billions of devices in real time, all while enabling them to reassign 70% of the expensive engineering talent previously focused on Kafka to projects that move the needle for the business. The durability of our growth is also reflected in our ability to rapidly expand once we land a customer. A great illustration of this dynamic is one of our largest Confluent Cloud customers. As one of the highest traffic job websites in the world this customer sends more than 4.5 gigabytes per second through Confluent Cloud every day. Kafka was a no-brainer choice to start their data in motion journey but they soon found themselves spending too much developer time managing Kafka. Our commercial relationship started with a small deal in 2020 for a single use case in a single business unit. As that pilot proved successful, we landed a $1 million-plus deal that expanded Confluent Cloud to more business units across the company. Inspired by our platform's extensive capabilities and an accelerated move to the cloud our customers reimagined their data architecture in 2021, leading to our first multimillion dollar deal with this customer. As Confluent became a critical unified data layer across the organization, their annual spend surpassed $8 million. As you can see what often starts as a small land for a single use case can rapidly expand to a large customer in just two years' time. But we believe we are still at the beginning of a great partnership as use cases and streaming data become more pervasive throughout the organization. Turning to efficiency. On a year-over-year basis we improved non-GAAP operating margin by 14 points in Q3 and eight points in Q2. We are pleased with the substantial margin improvement we've driven this quarter and excited that there are significant opportunities to continue these improvements. We're making substantial progress in creating strong connective tissue between our product-led and enterprise sales motions to help accelerate our customers' time to value. And we are still early in leveraging our partner ecosystem and bringing to bear a solution and industry focus. As an eight-year-old company, we believe our go-to-market model will drive differentiation and separation from our competitors which will generate greater leverage and efficiency in our model over time. Looking forward, we remain confident in our ability to achieve positive non-GAAP operating margin when we exit Q4 2024 Confluent's 10-year mark as a company. A profitability time line comparable to many of our successful high-growth peers. And finally we're pleased to announce that Rey Perez has joined Confluent as our Chief Customer Officer. Rey joins us from New Relic, where he most recently held the role of CCO leading the solutions engineering, solutions architecture, enablement and expert services teams. We'd also like to thank Roger Scott for his leadership and impact while at Confluent and wish him the best in his next chapter. To summarize, we have entered the data streaming era. Kafka is at the center of this movement, but represents just the foundation of the emerging platform. Our recent releases of Stream Designer and Stream Governance are great examples of this and show how Confluent is moving up the stack to help our customers connect, process, store, govern and share data from across their businesses. We believe this model will be the basis to drive continued durable and efficient growth for our business and allow us to capture the lion's share of our large market opportunity ahead. With that, I'll turn the call over to Steffan to walk through the financials.