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Salary: USD 120,000 - 130,000 / annual
Soda is a data quality monitoring platform used by Fortune 500 companies (Disney, Ralph Lauren, CBRE, HelloFresh, 2K Games, Nubank) and open-source users (Tesla, Slack, Adyen, Walmart, JPMC). The company is building the first Data & AI performance monitoring platform spanning data collection to automated decision-making.
You will own content end-to-end at Soda: the website, technical blogs, webinars, product videos, and your own audience. The core challenge is not creating individual pieces but building a system that scales content production. You'll work with founders who have spent a decade debating this category, customer engineers embedded in Fortune 500 data pipelines, and customers with unpublished expertise. Your job is extracting that knowledge and publishing it at volume—only possible with automation behind it.
This role bridges technical content creation and growth engineering. You'll write code daily (Python/TypeScript, APIs, webhooks, databases, agentic workflows with Claude and MCP servers) but this is not a software engineering position. You're not building product; you're building the content engine. You'll also write extensively for a technical audience (Staff Data Engineers, CDOs), so credibility on data pipelines, quality, and warehouses is essential. The bar is taste and anti-slop: you'll bin accurate but lifeless drafts, diagnose why the pipeline produced them, and fix the system. Output should sound like its author (founder vs. customer engineer), not like a model. Your own profile and following validate that the engine works.
You report to a co-founder. The team is distributed across 12+ countries, fully async, with offices in Brooklyn available if desired. This is a high-autonomy role: you find constraints and fix them; nothing should be blocked waiting for an engineer.
REQUIREMENTS:
- Demonstrable technical writing ability: must provide a portfolio piece that a technical audience actually read (described as "the most important line in this ad")
- Serious prompt engineering: multi-step LLM pipelines with structured outputs and evals; understanding of why naive approaches produce poor results
- 3+ years building things that go to market: technical content, growth engineering, marketing engineering, GTM engineering, DevRel, marketing ops, RevOps, or founder background
- Code autonomy: Python or TypeScript, APIs, webhooks, databases; ability to work independently without blocking on engineers; familiarity with agentic workflows (Claude Code, MCP servers, agent skills)
- Data fluency: credible discussing pipelines, data quality, and warehouses with Staff-level engineers and CDOs
- Generalist mindset: identify constraints and fix them; prefer building workflows over repetitive manual tasks
- Independence: thrive in distributed, async teams across 12+ countries without hand-holding
- Fluent English
GOOD TO HAVE:
- Grown a technical audience (newsletter, blog, LinkedIn, open source, YouTube)
- Content, growth, or DevRel experience in data, developer tools, or infrastructure
- Shipped agentic workflows to real users (MCP servers, Claude skills, tool-using agents)
- Video production skills (shooting and editing)
- Industry experience in data quality, data management, or data observability
