Full-Stack Marketer
Nexla
Full-Stack Marketer
Nexla
No-code data integration for AI
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.
Role
You would own Nexla’s digital presence end to end: SEO, paid, the website funnel, marketing automation, and the content that feeds all of it. The audience is data engineers, IT leaders, and enterprise decision-makers, so the work is as much about understanding data integration as it is about channels. You are part of a global team, which means you set the strategy and then execute it yourself. AI tooling is expected in the work, not treated as a shortcut.
The current marketing team has a technical product marketer role and a web/ brand manager. We use a few agencies to help with paid ad campaigns, website management and creatives for websites and campaigns.
What you'll own
AI tooling runs through all of it: content production, competitive and market research, segmentation, and the triggers behind automated campaigns.
Ninety days in: You would have helped revamp our inbound traffic and conversions to bring in more demo requests and help improve conversion.
The hard problem
Most of the traffic today is organic and direct. The hard problem is to make the website an organic lead engine consistently aligning top topics with content needed and SEO/ AEO optimization to drive more qualified traffic and better engagement and conversions. .
Must-haves
Bonus points
You'll thrive here if
One thing to be clear about: this is a hands-on generalist role. You set the strategy and then you write the copy, build the workflow, and pull the numbers yourself. There is no team underneath you to hand it to.
How we hire
4 conversations, about 3 weeks end to end.
The practical stuff
Bengaluru or remote within India. Compensation includes base salary and equity, set by depth and experience rather than by title. Overlap with US hours till noon pacific will help working with web dev team and ad agency.
Worth a look before you apply
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.
Nexla provides an enterprise-grade AI-powered data integration platform that turns data from any source into production-ready data products for AI applications, using a no-code/low-code interface. It unifies data integration, preparation, governance, and monitoring with 700+ pre-built connectors and supports ELT, ETL, streaming, APIs, and Retrieval-Augmented Generation, including Nexsets—virtual data products with semantic metadata, data quality checks, and lineage. The platform emphasizes automated, governed data layers for AI agents, offering end-to-end data governance and collaboration across technical and non-technical users. Its goal is to reduce data deployment times from months to days by turning raw data into ready-to-use data products for AI agents and applications.
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