C
Confluent
Staff Product Manager (Real Time Data Analytics Platform)
Skills
Product ManagementKafkaData AnalysisMachine LearningAIProduct Manager
2 months ago
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Important information
Contract type:
Permanent
Salary:
$231,500 – $272,000 / year
Location:
United States
Work mode:
Onsite
Published on:
29 August 2026
What they need
- 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
- 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- Strong technical judgment, comfortable navigating tradeoffs in distributed systems, storage engines, query performance, and cost optimization
- 8+ years of experience in product management or related technical roles
- Experience defining and launching products in the data infrastructure, analytics database, or stream processing space
- Deep familiarity with real-time analytics, database internals, or streaming data platform architecture, either through product management or hands-on engineering
- Track record of leading cross-functional initiatives with executive-level visibility and communicating technical strategy to senior leadership
- Bachelor’s degree or equivalent practical experience
- 5+ years of experience taking technical infrastructure or data products from conception to launch
- Engineering background in analytical databases, stream processing, or distributed storage systems
- Experience with real-time OLAP engines (e.g., ClickHouse, Apache Druid, StarRocks, Apache Pinot) as a builder, product manager, or power user
- Familiarity with Kafka, Flink, or similar streaming infrastructure
- Prior experience with cloud-native infrastructure products and consumption-based pricing models
- Experience working on open source data infrastructure projects
- Experience building products that serve AI/ML workloads for retrieval, feature serving, or context delivery
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