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Bengaluru, Karnataka, India
Hybrid
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.
Our Customer Success team owns the end-to-end client experience, from initial integration through enterprise adoption and expansion. It is a small team, and this role does two jobs at once: running your own enterprise accounts, and mentoring the CSMs around you. You work closely with Sales, Solutions, Engineering, and executive leadership, and you are the primary voice of the customer internally, including on where the platform goes next. As part of a lean and high-impact team, you will directly manage a portfolio of 10-15 strategic enterprise accounts, guiding them through both initial onboarding and long-term customer success.
AI tooling is part of the job here, not a side interest. Customer communication, health tracking, and account reporting are all places we expect you to use it.
Ninety days : You will have fully onboarded your initial pod of strategic enterprise accounts, audited and leveled up our CS playbooks. You will have joined key customer calls to assess health across your 10–15 enterprise accounts, identified the next set of strategic and tactical deliverables for each, and established clear coaching cadence and updated playbooks for the team.
Because Nexla’s enterprise platform bridges deep data engineering (ETL/ELT, APIs, streaming) with cutting-edge AI architectures (agentic workflows, RAG, and MCP servers), the core tension in this role is balancing technical execution with high-level value creation.
Bengaluru, hybrid, 2 days a week in office. Compensation includes base salary, bonus, and equity, set by depth and experience rather than by title. Working hours: requires early hours of US West Coast coverage.
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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