Staff Product Manager, Real Time Data Analytics Platform

We’re not just building better tech. We’re rewriting how data moves and what the world can do with it. With Confluent, data doesn’t sit still. Our platform puts information in motion, streaming in near real-time so companies can react faster, build smarter, and deliver experiences as dynamic as the world around them.

It takes a certain kind of person to join this team. Those who ask hard questions, give honest feedback, and show up for each other. No egos, no solo acts. Just smart, curious humans pushing toward something bigger, together.

One Confluent. One Team. One Data Streaming Platform.

About the Role:

Today, enterprises stitch together separate systems for streaming, analytics, and serving. Each of these systems come with its own latency, cost, and operational overhead. The next evolution of Confluent's platform collapses that complexity by continuously materializing streaming data into fast, queryable state that serves real-time analytics, operational applications, and downstream intelligent systems from a single foundation.

As a Staff Product Manager, you'll own the vision, strategy, and roadmap for the core streaming analytics engine underlying Confluent's real-time data platform. This system sits at the intersection of stream processing, real-time analytical databases, and modern serving infrastructure, continuously transforming data in motion into low-latency, queryable materialized views that power AI agents, dashboards, operational queries, and event-driven applications. You'll own the engine: the storage, materialization, query layer, and performance, while partnering with peer product managers who own the downstream product experiences built on top of it.

This is a foundational, high-leverage role. You'll define a new product category that brings together concepts from streaming analytics, real-time OLAP, materialized views, and modern database systems into a cloud-native, streaming-first architecture. You'll partner directly with engineering, design, and senior leadership to bring this to market and position Confluent as a compelling alternative to standalone real-time analytical databases.

What You Will Do:

Own the product strategy for Confluent's streaming analytics engine: storage, materialization, query serving, performance, and developer experience.

Develop competitive strategy against established real-time analytical databases, articulating Confluent's differentiated value as a streaming-native alternative.

Drive key architectural and product tradeoffs across consistency, latency, query performance, cost, and developer ergonomics.

Partner deeply with engineering to navigate the technical complexity of building a new cloud-native data system at scale.

Collaborate with peer product managers who own downstream experiences including AI, context delivery, and application integration, to ensure the engine meets the needs of the products built on it.

Work with customers and design partners to validate use cases across real-time analytics, operational applications, and AI, shaping the developer experience and refining go-to-market positioning.

Collaborate with Confluent's broader product and platform teams to ensure tight integration with Kafka, Flink, and Confluent Cloud.

Define success metrics, cost models, and pricing strategy for a new product area.

Influence Confluent's company-wide platform strategy through executive-level communication and cross-functional leadership.

What You Will Bring:

Bachelor's degree or equivalent practical experience.

8+ years of experience in product management or related technical roles.

5+ years of experience taking technical infrastructure or data products from conception to launch.

Deep familiarity with real-time analytics, database internals, or streaming data platform architecture, either through product management or hands-on engineering.

Experience defining and launching products in the data infrastructure, analytics database, or stream processing space.

Strong technical judgment, comfortable navigating tradeoffs in distributed systems, storage engines, query performance, and cost optimization.