Solutions Engineer

Solutions Engineering is where Carbon Arc meets its customers. We work directly with the investors, strategists, researchers, and builders by helping them integrate Carbon Arc into their stack, build on top of it, and get real value out of our growing data asset catalog unified under a single ontology. We're the technical partner who turns "here's our data platform" into "here's the thing you actually needed."

We're hiring a Solutions Engineer who is equally comfortable with code, data, and customers. You'll partner with customers across their lifecycle, occasionally helping prove out the platform during a sales cycle, but mostly after the deal is signed: leading integrations, building the agents, notebooks, dashboards, and reference implementations that show what's possible, and helping customers' own teams build confidently on top of Carbon Arc.

The role is high-trust and high-autonomy. You'll often be a customer's main technical point of contact, focused on getting them successfully deployed and self-sufficient. We want strong engineers who like being close to customers and are energized by making someone else's problem disappear.

• Own customer integrations end to end: scope the approach, design how Carbon Arc fits their environment (catalog access, identity-bound consumption, MCP, SSO), and deliver a working solution alongside their users, engineers, and leadership
• Show what's possible with Carbon Arc: agents, RAG and graph-based retrieval over the Carbon Arc knowledge graph, MCP-driven workflows, notebooks, dashboards, and natural-language interfaces against the catalog
• Enable customers to stand on their own: pair with their engineers, write the docs and examples they need, and clear blockers as they surface during deployment
• Identify each customer's real needs, then turn what works into reproducible motions: patterns, templates, and reference solutions that scale
• Drive adoption and time-to-value, stepping into sales and partnership conversations when the technical stakes call for it
• Close the loop with product and engineering: bring feedback back from real deployments and influence what we build next

• 3+ years shipping production software or solutions close to customers (solutions engineering, implementation or integration engineering, forward deployed engineering, applied work, or similar)
• Strong written and verbal communication. You can run a customer call, write a clear design doc, and produce a clean README in the same day. You're comfortable being the technical face of Carbon Arc to a customer's engineers and executives
• Strong proficiency in at least one general-purpose programming language (Python preferred), and the willingness to be useful in whatever language the customer is already using
• Strong data skills: comfort with SQL, a taste for what clean data looks like, instincts for joining and shaping messy datasets, and the ability to drop into a notebook and produce something useful within an hour
• A working understanding of the modern data landscape, including formats (Parquet, Iceberg, Arrow), warehouses and lake houses, catalogs, data contracts, and common access patterns
• Hands‑on AI experience. You've built applications with LLMs, shipped agents, run into RAG's failure modes firsthand, and have a feel for what's production-ready versus what's just a demo
• A track record of taking an ambiguous customer problem from first conversation to a working, adopted solution with minimal supervision
• A bias toward customer outcomes: you measure success by whether the customer got value, not by how much you shipped, and have a low tolerance for hand‑waving

• Serious time spent inside real datasets: financial data, behavioral data, or something similar. You can speak to use cases, common gotchas, and what good looks like
• Experience building good data engineering systems: well-structured pipelines, sound data models, and integrations that are reliable, maintainable, and easy to operate in production
• Familiarity with semantic data modeling and graphs: ontologies, knowledge graphs, entity resolution, and graph databases (Neo4j or similar), and a feel for how semantics…