Full-Stack Marketer

### **Nexla, briefly** 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. **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** - Organic visibility for data integration and AI keywords. SEO strategy, the technical work behind it, and the content that actually ranks. - Paid and ABM. Google Ads and LinkedIn Ads from audience targeting through creative, plus the account-based programmes aimed at enterprise prospects. - The funnel. Website performance and user behaviour, where people drop and why, the marketing automation workflows and lead scoring behind it, and the reporting that says which channel earns more budget and which one stops. - The content engine. Blogs, whitepapers, case studies, social across LinkedIn and X, video scripts, and webinar material. Technical enough that a data engineer keeps reading, clear enough that the person signing the contract finishes it. *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** - 2 to 5 years in digital marketing, content marketing, or a closely related role, in B2B. - Hands-on SEO/ AEO, SEM, and web analytics. You have made decisions from Google Analytics, not just reported from it. - A marketing automation platform such as HubSpot or Marketo, including building the workflows yourself. - Brand, email and website optimization experience. - Nice to have - Paid campaign experience on Google Ads and LinkedIn Ads, covering both audience targeting and creative. - Strong written English and a solid grasp of B2B marketing and enterprise sales cycles. - You use AI tools in your actual work and can say specifically where they help and where they mislead you. **Bonus points** - SaaS, data technology, or enterprise software marketing, or working knowledge of data integration, analytics, or AI and ML concepts. - Marketing attribution and revenue operations. - Design tools (Canva, Figma, Adobe Creative Suite), or video creation and editing. - A bachelor’s degree in marketing, communications, business, or a related field. **You'll thrive here if** - You reach for AI on every task by default, and you can tell when it has produced something worth shipping. - You argue from numbers. You would rather kill a campaign that is not working than defend it. - You can write something a data engineer respects and an executive finishes. - You work across time zones without needing everything to happen in a meeting. *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. - **Hiring manager interview.** Current Experience, accomplishments, overview of team and role - **Peer interview.** Experience and accomplishments, AI projects - **Engineering interview.** Ability to articulate impact, AI first mindset and work with engineers - **Panel. Presentation of a 30-60-90** day plan based on understanding of what needs are. **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** - **What we are building now.** [MCP Studio](https://nexla.com/mcp-studio/) and [Express](https://nexla.com/express/), our latest innovation. - **Who uses us, and why.** [](https://nexla.com/customers/) - **What we publish.** [](https://nexla.com/blog/) - **Our CEO on why we take "Remember to Relax" seriously.** [Read the founder story](https://nexla.com/what-we-value-at-nexla/) - **Everything else.** [](https://nexla.com/) · [All open roles](https://nexla.com/jobs/) ### Why Nexla? Why now? 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, 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.