Product Manager, Bay Area

Nexla is the data layer for enterprise AI. We give AI apps and agents the connectivity, context, and governance to work across more than 1,000 enterprise systems in real time, and we process over a trillion records a month doing it. DoorDash, LinkedIn, Johnson & Johnson, Instacart, and LiveRamp run mission-critical data on us. Honorable Mention in the Gartner Magic Quadrant™ for Data Integration Tools, and top-rated by customers on Gartner Peer Insights four years running. Founded 2016, remote-first, headquartered in San Mateo.

Our values are short and we actually use them: Have Empathy. Be Curious. Be Intellectually Honest. Achieve Excellence. Remember to Relax.

• →4 - 8 years of experience in Product Management, Product Operations, Technical Program Management, Solutions Engineering, or related technical SaaS roles.
• →Strong technical fluency experience operating in product environments involving APIs, integrations, distributed systems, data infrastructure, developer platforms, or AI/LLM systems.
• →Ability to engage deeply with engineers and participate meaningfully in technical product discussions.
• →Strong customer empathy and a track record of turning customer problems into product improvements.
• →Excellent communication, organizational, and cross-functional collaboration skills.
• →Ability to operate effectively in fast-moving, ambiguous environments with high ownership.
• →Strong prioritization skills and a bias toward action.
• →Experience with AI-native workflows, conversational interfaces, agents, MCP ecosystems, or developer-focused products is a plus.
• →Both strategic and hands-on, comfortable coordinating across teams while also prototyping ideas, exploring workflows, researching problems, and accelerating product learning cycles directly.

• Experience in data infrastructure, integrations, APIs, AI platforms, or developer tooling
• Experience working closely with enterprise customers and technical stakeholders
• Familiarity with ETL/ELT, streaming systems, APIs, or distributed architectures
• Experience operating in fast-moving startup or platform environments
• Strong understanding of developer experience and enterprise workflow design
• Experience collaborating closely with engineering leadership and cross-functional technical teams

Compensation
Compensation for this role will be determined by overall skills, experience, and location. The salary range for a US based Product Manager will be $150,000 - $180,000 USD. The package will also include benefits such as Medical, Dental, and Vision, 401k, and flexible PTO.

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.