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Salary: USD 80,000 - 160,000 / annual
Nexla is the data connectivity layer for enterprise AI, processing over a trillion records monthly for customers including DoorDash, LinkedIn, and Johnson & Johnson. The company provides 1,000+ bidirectional connectors and agentic data integration capabilities, with Helix—a context engine that learns from metadata and schemas to keep pipelines reliable.
As a Forward Deployed Engineer, you will embed with Nexla's most strategic customers to understand their complex data environments, build solutions on the Nexla platform, and deploy them to production. This is a hands-on, engineering-first role where you own the full technical delivery lifecycle: scoping, building, testing, deploying, and supporting production pipelines, connectors, and workflows across warehouses, SaaS apps, streaming systems, and internal APIs.
You will tackle the integrations that others cannot solve—undocumented APIs, schemas that change without warning, legacy systems, and regulatory constraints—using intelligent connectors, context-aware transformations, and agentic capabilities in real customer environments. You will identify patterns across engagements and feed learnings back to Product and Engineering to build reusable connectors, templates, reference architectures, and playbooks. You will also support Sales through high-stakes technical evaluations, proof-of-value scoping, and serve as a trusted technical partner to customer stakeholders ranging from individual data engineers to VPs of Data.
Time allocation: approximately 60% building and deploying at customer sites, 25% contributing insights back to the product, and 15% partnering with Sales and Customer Success. This is a new function at Nexla, so you will have real input into how forward-deployed engineering is structured. When something breaks at a customer site, you are the first responder.
The role is based in San Mateo, California, hybrid with travel to customer sites up to 25% of the time.
**Requirements:**
- 0 to 4 years of software engineering experience with production systems you built and shipped yourself. Former technical founders and engineers with consulting backgrounds fit well.
- Strong Python proficiency: building data pipelines, writing integrations, working with APIs, and scripting through ambiguous problems.
- Hands-on experience with AI and LLM tooling: prompt engineering, agent development, orchestration frameworks, or AI-powered workflows. You do not need to be an ML researcher but must build with these tools.
- Real knowledge of enterprise data infrastructure: warehouses (Snowflake, Databricks, BigQuery), streaming platforms (Kafka), ETL/ELT patterns, and the modern data stack.
- Customer-facing confidence: ability to run technical discovery calls, whiteboard architectures with customer data teams, and explain tradeoffs to non-technical stakeholders.
**Bonus qualifications:**
- Experience building or deploying agentic AI systems in enterprise environments.
- Time at a data integration, iPaaS, or ELT company (Fivetran, MuleSoft, Informatica, Airbyte) or 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.
