Forward Deployed Engineer, Data Integration(USA)

Nexla•San Mateo, CA

13h•$80,000 - $160,000•Hybrid

About The Position

Nexla is hiring a hands-on, engineering-first Forward Deployed Engineer to embed with strategic customers and deliver production data integrations. The role involves understanding customer needs, building solutions on Nexla, and getting them into production across complex environments with fragmented systems, legacy pipelines, compliance constraints, and tight timelines. The engineer scopes, builds, tests, deploys, and maintains pipelines, connectors, and workflows spanning data warehouses, SaaS applications, streaming systems, and internal APIs. The role is not pre-sales or solution architecture; the engineer writes and deploys code and is the first responder when customer-site systems break. The engineer will solve difficult integration problems involving undocumented APIs, changing schemas, legacy systems, and regulatory constraints, using intelligent connectors, context-aware transformations, and agentic capabilities. They will identify recurring patterns across engagements and share product gaps and customer feedback with Product and Engineering, while contributing reusable connectors, templates, reference architectures, and playbooks. They will also support high-stakes technical evaluations, scope proofs of value, and partner with Sales and Customer Success. Work is described as approximately 60% building and deploying at customer sites, 25% feeding field learnings into the product, and 15% partnering with Sales and Customer Success. This is a new function at Nexla, with no established playbook or escalation bench. The role requires judgment about which customer-specific solutions should remain one-offs and which should become reusable product capabilities. The position is based in San Mateo, California, in a hybrid arrangement, with travel to customer sites up to 25% of the time. Compensation for US-based candidates is $80,000 to $160,000, plus equity, medical, dental, vision, 401(k), and flexible PTO.

Requirements

0 to 4 years of software engineering experience, including production systems personally built and shipped.
Strong Python skills, including building data pipelines, writing integrations, working with APIs, and scripting through ambiguous problems.
Hands-on experience with AI and LLM tooling, such as prompt engineering, agent development, orchestration frameworks, or AI-powered workflows.
Knowledge of enterprise data infrastructure, including data warehouses such as Snowflake, Databricks, and BigQuery; streaming platforms such as Kafka; ETL and ELT patterns; and the modern data stack.
Confidence in customer-facing settings, including running technical discovery calls, whiteboarding architectures, and explaining tradeoffs to non-technical stakeholders.
Ability to take ownership and deliver working outcomes in real customer environments.
Ability to work in a new function without an established playbook or escalation bench, and to act as the first responder when customer-site systems break.

Nice To Haves

Experience building or deploying agentic AI systems in enterprise environments.
Experience 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 such as CDC, GraphQL, gRPC, webhooks, and REST.
Cloud-native experience on AWS or GCP, including managed data services, IAM, and networking.
Experience as a technical founder or in a consulting background.

Responsibilities

Scope, build, test, deploy, and maintain production pipelines, connectors, and workflows for strategic customers.
Integrate customer warehouses, SaaS applications, streaming systems, and internal APIs, including difficult or undocumented systems and changing schemas.
Respond first when deployed systems break at customer sites.
Identify recurring patterns and product gaps across customer engagements, and communicate them to Product and Engineering.
Contribute reusable connectors, templates, reference architectures, and playbooks to the platform.
Support high-stakes technical evaluations and scope proofs of value.
Partner with Sales and Customer Success and build trusted relationships with customer stakeholders, from data engineers to VPs of Data.
Run technical discovery calls, whiteboard architectures with customer data teams, and explain tradeoffs to non-technical stakeholders.