Nexla is the data connectivity layer for enterprise AI. Over 1,000 bidirectional connectors give agents real-time access to data and actions over MCP. Nexla's Agentic Data Integration handles high-volume streaming ETL, CDC, and Document ETL. At the core of the platform is Helix, a context engine that continuously learns from metadata, schemas, telemetry, and documentation to give agents accurate context and keep pipelines reliable. Nexla processes over a trillion records a month for enterprises including DoorDash, LiveRamp, LinkedIn, and J&J. Honorable Mention in the Gartner Magic Quadrant™ for Data Integration Tools, and top-rated by customers on Gartner Peer Insights four years running.
You’ll own the technical relationship with our accounts: onboarding, implementation, adoption, escalations, and everything that happens when a pipeline that used to work stops working.
You are the person the customer's data team calls, and the person who tells them the truth when the answer is inconvenient. Most of the job is conversation: understanding what they are actually trying to do, explaining what the platform will and will not do for them, keeping their expectations aligned with reality, and raising problems while they are still small.
The technical part matters, but not in the way people usually assume. We want a generalist who has built things before and therefore understands how the pieces fit together: what a Kubernetes cluster is for, why a VPN tunnel might be the bottleneck, what a stack trace is telling you, what a slow query looks like. You will not be asked to be the deepest engineer in the room. You will be asked to be the person who can talk to the deepest engineer in the room, understand them, and translate what they said to a VP who has thirty seconds.
You should be a generalist who has built software or data systems at some point. Not necessarily an expert in any one of these, but genuinely conversant in most of them, in the way someone is conversant after having actually worked with them:
A few large companies - DoorDash, SentinelOne, Johnson & Johnson, LinkedIn, Amex, Integrity Marketing, among them use Nexla for data integration. Connectors, runtime, transformations, scheduling, the parts of the stack where data has to move between systems reliably.
The reason this is an interesting moment to join is what the agent shift is doing to the category. Data integration used to mean "land this data in that warehouse so a human can look at it." That product is mature. What it's becoming is closer to "an agent asks a business question, and the platform figures out which data and what code and which APIs add up to a real answer." That's a much bigger problem, and most of the architecture for it hasn't been built yet by anyone.
A concrete example. A revenue team wants to ask "which enterprise customers are showing renewal risk" and get a real answer through whatever agent or app they work in. Today there is no clean way to answer that question, it requires CRM, usage, support, and billing data joined in org-specific ways, and the calling agent can't just invoke a tool that returns the right answer unless someone first establishes whether the underlying data is even capable of producing one.
That is the system we are building. A probe agent investigates the data plane whether the right fields are populated, whether freshness is adequate, whether the joins exist, whether credentials cover the required scope and returns a grounded feasibility answer. Where there are gaps, Express.dev composes the pipelines to close them. The capability is then exposed as an MCP server: "renewal risk" as a curated product, with the business logic correct and the query semantics described well enough that the calling agent uses it as intended. The MCP Gateway governs which agents can call which capabilities, with the policies, audit, and observability an enterprise control plane requires.
The bet is that ten years of connector work, an enterprise customer base, and a runtime that already moves real volume are the right foundation to define this layer from.
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