Growth Engineer

About Dragonfly

Dragonfly is a high-performance, Redis-compatible in-memory data store built for real-time, large-scale, intelligent workloads. Our customers run Dragonfly at the core of ML feature stores, personalization systems, and real-time recommendation engines where latency and scale are non-negotiable.

We're building infrastructure for the next generation of ML and AI systems. Growth in this market isn't won by spending more on ads or shipping more landing pages. It's won by engineers who understand the product deeply enough to make adoption feel inevitable: who can sit inside the funnel, the docs, the benchmarks, and the agentic surface area, and engineer growth the way you'd engineer a system.

Role Overview

Most companies treat growth as a marketing problem and engineering as a separate org that occasionally gets pulled in. That model is too slow for an infrastructure product competing in a crowded market.

As a Growth Engineer, you'll own the technical surfaces that drive how developers discover, evaluate, adopt, and grow with Dragonfly. You write code, you have real opinions about developer experience, and you understand where the agentic landscape is heading. You'll build the SEO and GEO programs that make Dragonfly the answer engineers and their AI agents surface, ship and harden Dragonfly's MCP and other agentic-enablement work, build and publish the benchmarks that win technical arguments, and run conversion optimization across the funnel.

This is the perfect role for an engineer who'd rather move a revenue or adoption number than close a ticket. You'll measure your work in qualified signups, activation, and pipeline, not in lines shipped.

What Makes This Role Different

You will build and manage an agent team

This is not "use AI to write faster." You will design, train, and manage a system of AI agents that extend your output across content production, technical SEO, competitive monitoring, benchmark generation, and funnel experimentation. Think of it as building a small growth engineering team where you are the architect and the agents are the operators.

You'll architect that system, build its evaluation loops, iterate on it, and own everything it ships. The bar for output scales accordingly.

You will make Dragonfly the default for agentic workloads

AI agents are becoming a primary consumer of infrastructure decisions. When an agent is wiring up a cache, a feature store, or a real-time data layer, Dragonfly should be the obvious, well-documented, frictionless choice. You'll own Dragonfly MCP and the broader agentic-enablement surface: the tooling, integrations, schemas, and developer ergonomics that let both humans and agents adopt Dragonfly without friction. This is a growth lever that most infrastructure companies haven't even noticed yet.

Growth is an engineering discipline here

You'll treat SEO, GEO, conversion, and developer onboarding as systems to be instrumented and improved, not campaigns to be run. You should be as comfortable reading a query plan or a benchmark harness as you are reading a funnel report.

Ownership and Responsibilities

SEO and GEO Programs

You own how Dragonfly gets discovered, by humans through search and by AI systems through generative engines.

Build and run technical SEO: content architecture, performance, structured data, internal linking, and the programmatic pages that capture high-intent developer queries
Own GEO (generative engine optimization): make Dragonfly accurately and prominently represented in the answers LLMs and AI search tools give engineers evaluating in-memory data stores
Build the tooling and agent pipelines that scale content production and monitoring without sacrificing technical accuracy

Agentic Enablement and Dragonfly MCP

Own and evolve Dragonfly MCP and related integrations that let AI agents discover, configure, and operate Dragonfly
Reduce friction for agent-driven adoption: documentation, schemas, examples, and ergonomics that work for both humans and machines
Identify where the agentic landscape is heading and position Dragonfly ahead of it

Integrations with Popular Development Tooling

Build and maintain integrations that put Dragonfly directly in the workflows developers already use: frameworks, ORMs, client libraries, deployment platforms, observability tools, and the rest of the modern stack
Lower the cost of adoption to near zero by meeting developers where they already work, so choosing Dragonfly is the path of least resistance
Prioritize integrations by adoption and growth impact, ship them, and keep them healthy as the ecosystem moves

Benchmarks and Technical Proof

Build, run, and publish benchmarks that hold up to scrutiny from senior infrastructure engineers
Turn benchmark results into credible, shareable artifacts that win technical evaluations against Redis, Valkey, ElastiCache, and others
Build the harnesses and automation that make benchmarking repeatable, not a one-off heroic effort

Conversion Rate Optimization

Instrument and optimize the funnel end to end: site, docs, signup, and activation
Design and ship experiments tied to revenue and user growth, and own the results
Close the gap between "engineer is interested" and "engineer is running Dragonfly in production"

Developer Experience

Identify friction in the developer journey and fix it in code, not in a backlog
Turn real community questions and drop-off points into durable improvements that compound
Work directly with engineering, marketing, and community to get the technical details right

What We're Looking For

Non-negotiables

You write code: you're a real engineer, comfortable shipping in production and reasoning about systems. You don't need an engineer to implement your ideas for you.
Developer experience instinct: you have strong, defensible opinions about what makes a tool a joy or a pain to adopt, and you've acted on them.
AI-native operator: you don't just use AI tools. You build with them. You've designed agent systems, written prompts at scale, and thought seriously about output quality and evaluation.
Agentic fluency: you understand where the agentic landscape is going, what MCP is for, and why agent-driven adoption is about to matter for infrastructure.
Growth orientation: you're motivated by moving revenue and adoption numbers, and you can connect technical work to those outcomes.

Strong signals

You've built something in public that got traction with an engineering audience
You've shipped SEO or GEO programs that drove measurable, durable developer traffic
You've built or contributed to MCP servers, agent tooling, or automated pipelines beyond basic prompt chaining
You have opinions about the Valkey fork, the future of managed Redis, or the ML feature serving stack
You've published benchmarks or technical content that changed how people evaluated a product

KPIs and Success Metrics

Qualified developer acquisition: growth in high-intent traffic and signups from search, GEO, and agent-driven discovery
Activation and conversion: measurable improvement in the journey from first touch to production usage
Agentic adoption: Dragonfly's presence and ease of use across MCP and agent-driven workflows
Pipeline impact: shorter evaluation cycles and higher deal velocity tied to benchmarks and technical proof
Agent system output: measurable increase in growth output and reach without a proportional increase in manual effort

Why This Role Matters

Infrastructure is a crowded, noisy, conservative market, and the way engineers discover and evaluate tools is changing fast. Search is being reshaped by generative engines, and agents are starting to make real adoption decisions. The companies that win will be the ones that engineer for that shift early instead of treating growth as a marketing afterthought.

This role owns whether Dragonfly is the obvious choice at the moment an engineer, or their agent, is deciding what to build on. That's one of the highest-leverage problems at the company, and you'll own it with real autonomy and visibility.

Location: Remote To apply: careers@dragonflydb.io