Senior Technical Marketing Manager (TMM)- Confluent
Senior Technical Marketing Manager (TMM)- Confluent
Posted today
SENIORITY
Lead
About the role
- Build flagship demos and solutions
- Design and implement realistic, reusable demos and reference solutions that showcase Confluent's Data Streaming Platform (Kafka, Flink, Iceberg, connectors, governance, security and access control, AI/ML and AI agent use cases).
- Operationalize these demos so they are reproducible, documented, and easy for others to run (field, partners, and customers).
- Create technical content that tells a story
- Author high quality technical content (blogs, whitepapers, reference architectures, lab guides, how to videos, webinars) that explains why and how to use Confluent to solve specific problems and use cases.
- Translate complex distributed systems concepts, such as exactly once semantics and event driven architecture, into clear narratives that connect architecture to business outcomes for audiences from engineers to CTOs and CFOs.
- Develop Streamhouse messaging, reference architectures, and proof points that show how Confluent's Data Streaming Platform captures, processes, governs, and serves real time context to production applications and AI agents.
- Partner on launches, campaigns, and use cases
- Collaborate with Product Marketing and Product Management on launches and campaigns: define the technical story, proof points, and required assets.
- Develop content around priority use cases (e.g., real time analytics, app modernization, real time AI and AI/ML pipelines, data warehouse offload) and key industries such as financial services and e commerce.
- Measure the impact of your work through adoption, usage, and pipeline influence, and iterate based on what the data shows.
- Show up as a technical expert in the field
- Deliver live and virtual workshops, hands on labs, webinars, and conference talks that demonstrate Confluent in action.
- Support strategic customer and partner conversations with demos, architectural deep dives, and technical validation.
- Enable the sales and field teams with technical training, demo kits, and playbooks that help them win deals.
- Represent Confluent in briefings with industry analysts and technical influencers.
- Drive technical proof and competitive clarity
- Build and maintain benchmarks, comparisons, and technical proof points that articulate Confluent's differentiation vs. alternatives (self managed Kafka, CSP native services, other stream processing platforms).
- Create competitive battlecards and objection handling guidance, and contribute to win/loss analysis, from a practitioner's perspective.
- Feed insights back into the product
- Act as the voice of the practitioner: synthesize feedback from demos, events, customers, and the field to identify product gaps and improvements.
- Partner with Product Management to prioritize developer and operator needs on the roadmap.
- Hands on streaming and data experience
- 5+ years working with streaming/data technologies such as Apache Kafka, Apache Flink, Kafka Streams, Spark Structured Streaming, or similar.
- Strong understanding of distributed systems fundamentals, event driven architectures, and cloud native architectures.
- Solid software engineering skills
- Proficiency in at least one modern programming language (e.g., Java, Python) and strong SQL skills, with comfort writing production quality code, samples, and utilities.
- 5+ years of hands on experience with Kubernetes (K8s), plus Docker and other container platforms, for deploying and managing applications.
- Technical storytelling and content skills
- Demonstrated ability to create clear, accurate, and engaging technical content (blogs, talks, demos, labs, docs, or workshops).
- Talent for simplifying complex concepts without losing the important details, for both practitioner and executive audiences.
- Customer and field orientation
- Experience working directly with customers, sales, or solution teams (e.g., as a technical marketer, solutions architect, developer advocate, or sales engineer).
- Comfort gathering requirements, understanding real world constraints, and tailoring solutions and content accordingly.
- Collaboration and ownership
- Track record of end to end ownership: from idea prototype content launch iteration.
- Ability to work cross functionally across Product, Marketing, Engineering, and Field teams, balancing multiple stakeholders while keeping the practitioner and customer at the center.
- Self directed, comfortable managing multiple workstreams and making decisions without perfect information.
- Deep hands on experience with Confluent Cloud, Confluent Platform, and/or Apache Kafka at scale.
- Experience with Apache Flink (DataStream or Table/SQL APIs) or other stream processing engines in production.
- Familiarity with real time AI, AI agents, and agentic workflows built on streaming data (RAG pipelines, feature streams, context engineering, streaming ETL into vector/warehouse systems).
- Familiarity with open table and lakehouse formats such as Apache Iceberg, Delta Lake, and Apache Paimon.
- Experience creating and delivering hands on workshops (e.g., using Terraform, cloud labs, or self service environments).
- Experience building competitive battlecards or sales enablement that improved win rates.
- Familiarity with the Streamhouse architecture and with building real time context for AI agents and production applications on streaming data.
- Prior experience in Technical Marketing, Developer Relations, or Product Marketing at an infrastructure or data platform company.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
