Amazon Kinesis Data Streams (KDS) is a fully managed, serverless streaming data service. Customers use it to capture, process and store data streams at any scale. Tens of thousands of AWS customers run real-time workloads on KDS, including clickstream analytics, IoT telemetry, log and event pipelines, change data capture, fraud detection, and feeding data to generative AI and ML applications. We're looking for a Senior Product Manager – Technical to help set where real-time data streaming on AWS goes next.
You will own a major area of the KDS product, such as capacity modes and scaling, consumer experience (including enhanced fan-out), data retention, pricing and packaging, or integrations with the wider AWS analytics and AI stack. You will set the product vision, write the strategy and requirements, and work with engineering, UX, solutions architects, and go-to-market teams to deliver features that customers love and that grow the business. You will work backwards from customers and use data to make decisions. You will also represent KDS to customers, partners, and AWS leaders.
Key job responsibilities
Own the multi-year vision and roadmap for your KDS area. Write PR/FAQs, 6-pagers, and PRDs that line up engineering and leadership.
Go deep on customer problems through customer meetings, support trends, field feedback, and usage data. Turn what you learn into prioritized, measurable requirements.
Work with engineering on technical tradeoffs in distributed systems, including throughput, latency, durability, partitioning and sharding, and cost-to-serve.
Define and track the metrics for your area, such as adoption, revenue, usage by capacity mode and usage type, retention, and customer experience. Run business reviews (WBR/MBR) for it.
Drive pricing and packaging analysis, and model revenue and margin for new features.
Lead launches from start to finish with Product Marketing, Docs, Solutions Architects, and Sales. This covers re:Invent and other major launch moments.
Analyze competing streaming products (e.g., Apache Kafka and managed Kafka services, Confluent, cloud-native alternatives) and turn the findings into differentiated strategy.
Work across AWS teams (e.g., Amazon MSK, Amazon Data Firehose, AWS Lambda, Amazon Managed Service for Apache Flink, Amazon S3, Amazon SageMaker, Amazon Bedrock) to deliver an integrated customer experience.
