The posting
Nexla, briefly
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.
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 description Our customers run complex, high-stakes data environments: fragmented systems, legacy pipelines, compliance constraints, aggressive timelines. You would embed with the most strategic of them, work out what they actually need, build it on Nexla, and get it into production. This is a hands-on, engineering-first role, not pre-sales and not solution architecture. You write the code, you deploy it, and when it breaks at the customer site you are the first responder. You would be one of the first people in this function here, so you get a real say in how we do forward-deployed engineering.
What you’ll own
- Technical delivery at strategic customer sites, end to end. You scope it, build it, test it, deploy it, and keep it running. Production pipelines, connectors, and workflows across their warehouses, SaaS apps, streaming systems, and internal APIs.
- The integrations nobody else can make work. Undocumented APIs, schemas that change without warning, legacy systems, and regulatory constraints, solved with intelligent connectors, context-aware transformations, and agentic capabilities in real customer environments rather than in a deck.
- What the product learns from the field. You spot the patterns that repeat across engagements, take the gaps and friction straight to Product and Engineering, and contribute reusable connectors, templates, reference architectures, and playbooks back to the platform.
- The commercial edge of the work. Supporting Sales through high-stakes technical evaluations, scoping proof-of-value, and being a technical partner customer stakeholders trust, from individual data engineers up to VPs of Data.
Roughly 60% building and deploying at customer sites, 25% feeding what you learn back into the product, 15% partnering with Sales and Customer Success.
Ninety days in: [one sentence on what they will have shipped or changed by then].
The hard problem
Every engagement is a special case. Messy schemas, undocumented APIs, legacy systems, and regulatory constraints, all specific to one customer and all solved against their timeline. At the same time, the platform only improves if what you build for one customer becomes something we ship for everyone. Deciding which parts of an engagement are genuinely one-off and which should become a product is the judgment this role runs on.
Must-haves
- 0 to 4 years of software engineering, with production systems you built and shipped yourself. Former technical founders, and engineers with consulting backgrounds, fit well here.
- Strong Python. You build data pipelines, write integrations, work with APIs, and script your way through ambiguous problems.
- Hands-on with AI and LLM tooling: prompt engineering, agent development, orchestration frameworks, or AI-powered workflows. You do not need to be an ML researcher. You do need to build with these tools.
- Real knowledge of enterprise data infrastructure. Warehouses such as Snowflake, Databricks, and BigQuery, streaming platforms such as Kafka, ETL and ELT patterns, and the modern data stack. This is the part of the job most people underestimate.
- Customer-facing confidence. You can run a technical discovery call, whiteboard an architecture with a customer’s data team, and explain tradeoffs to non-technical stakeholders without dumbing them down.
Bonus points
- Experience building or deploying agentic AI systems in enterprise environments.
- Time at a data integration, iPaaS, or ELT company such as Fivetran, MuleSoft, Informatica, or Airbyte, or in another customer-facing engineering role.
- Familiarity with data protocols and patterns: CDC, GraphQL, gRPC, webhooks, REST.
- Cloud-native experience on AWS or GCP, including managed data services, IAM, and networking.
You’ll thrive here if
- You have high agency. The problem is not well defined, the playbook does not exist yet, and you move anyway.
- You have owned outcomes, not tasks. You have been the person responsible for making something work in a real environment, not a staging cluster.
- You translate in both directions, between a customer’s world and ours, and between an individual data engineer and a VP of Data.
- You get bored doing the same thing for months. No two engagements here are the same.
One thing to be clear about: this function is new here, so there is no playbook to inherit and no bench to escalate to. When something breaks at a customer site, you are the first responder.
How we hire
Four conversations, about 3 weeks end to end.
- Take-home assignment. What you build, scoped to roughly X hours.]
- Live vibe coding. Building something real with AI tooling in the loop, the way the job is actually done.
- System architecture, vibe coding. Designing a solution for a customer-shaped problem, again with AI in the loop.
- Customer strategy and relationships. How you run a discovery call, handle a stakeholder who disagrees with you, and keep an engagement on track.
On AI in our interviews. Two of these rounds are built around working with AI tooling, because that is how the job is done. Use it the way you would on an engagement, ours or anyone else’s. Watching you work without it would tell us nothing useful. What we dig into is your judgment: what you delegated, what you verified, and what you threw away.
You will hear from us either way.
The practical stuff
San Mateo, California, hybrid, with travel to customer sites up to 25% of the time.
Compensation is determined by overall skills, experience, and location. The range for a US-based Forward Deployed Engineer, AI is $80,000 to $160,000, plus equity, medical, dental, vision, 401k, and flexible PTO.
Worth a look before you apply
- What we are building now. MCP Studio and Express, our two newest bets.
- Who uses us, and why. nexla.com/customers
- The product in engineer terms. docs.nexla.com
- Our CEO on why we take "Remember to Relax" seriously. Read the founder story
- Everything else. nexla.com · Blog · All open roles
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, 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.
