Forward Deployed Engineer, Data Integration(USA)

<div class="content-intro"><h3><strong>Nexla, briefly</strong></h3> <p>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.</p></div><p><strong>Nexla, briefly<br></strong>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.</p> <p>Our values are short and we actually use them: Have Empathy. Be Curious. Be Intellectually Honest. Achieve Excellence. Remember to Relax.</p> <p><strong>Role description <br></strong>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.</p> <p><strong>What you’ll own</strong></p> <ul> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ul> <p><em>Roughly 60% building and deploying at customer sites, 25% feeding what you learn back into the product, 15% partnering with Sales and Customer Success.</em></p> <p><em>Ninety days in: [one sentence on what they will have shipped or changed by then].</em></p> <p><strong>The hard problem</strong></p> <p>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.</p> <p><strong>Must-haves</strong></p> <ul> <li>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.</li> <li>Strong Python. You build data pipelines, write integrations, work with APIs, and script your way through ambiguous problems.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ul> <p><strong>Bonus points</strong></p> <ul> <li>Experience building or deploying agentic AI systems in enterprise environments.</li> <li>Time at a data integration, iPaaS, or ELT company such as Fivetran, MuleSoft, Informatica, or Airbyte, or in another customer-facing engineering role.</li> <li>Familiarity with data protocols and patterns: CDC, GraphQL, gRPC, webhooks, REST.</li> <li>Cloud-native experience on AWS or GCP, including managed data services, IAM, and networking.</li> &