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Nexla is the data connectivity layer for enterprise AI, processing over a trillion records monthly for customers including DoorDash, LiveRamp, LinkedIn, and J&J. The platform provides 1,000+ bidirectional connectors, agentic data integration for streaming ETL and CDC, and Helix—a context engine that learns from metadata and schemas to keep pipelines reliable. Nexla is an Honorable Mention in the Gartner Magic Quadrant for Data Integration Tools.
As Senior Solutions Architect, you will own the technical relationship with a portfolio of enterprise accounts throughout their lifecycle: onboarding, implementation, adoption, troubleshooting, and escalations. You are the continuity point—the person customers' data teams call when pipelines fail or expectations need realignment.
The role is primarily conversational and relationship-driven. You will understand what customers are actually trying to build, explain what the platform will and will not do, keep expectations aligned with reality, and raise problems early while they remain small. You translate between technical engineers, security reviewers, and executive stakeholders without losing any audience. You troubleshoot alongside customers on failed runs, schema drift, connector failures, authentication issues, connectivity problems, and performance degradation, performing first-line diagnosis and escalating to engineering with clear reproductions when needed.
You will lead onboarding and implementation, design integrations with customers, guide projects to production, and handle difficult conversations with calm and credibility—missed dates, degraded pipelines, unexpected limitations, or upset customers. You flag adoption risks early and in writing (stalling adoption, departed champions, lost sponsorship, unscalable architecture). You mentor other Solutions Architects on customer handling, not just problem-solving. You partner with Customer Success and Support on renewal risk and hand expansion signals to Sales.
The technical part matters, but as a generalist translator, not as the deepest engineer. You need to understand how pieces fit together: what Kubernetes is for, why a VPN tunnel might be a bottleneck, what a stack trace tells you, what a slow query looks like. You will not be asked to be the deepest engineer in the room; you will be asked to talk to that engineer, understand them, and translate to a VP with thirty seconds.
This is an interesting moment to join because the agent shift is redefining the data integration category. Data integration is evolving from "land this data in that warehouse so a human can look at it" to "an agent asks a business question, and the platform figures out which data, code, and APIs produce a real answer." Nexla is building the system to enable this—probe agents investigating data feasibility, Express.dev composing pipelines to close gaps, and MCP servers exposing curated capabilities with correct business logic and governance.
**Requirements:**
- 5+ years in technical customer-facing work: solutions architecture, implementation, technical account management, professional services, consulting, support engineering, or data engineering with heavy stakeholder contact
- At least some hands-on building experience, not only advising
- Experience owning accounts or projects through delivery, not just handing off
- Communication skills: explain technical concepts to non-technical stakeholders without dumbing down or losing them; handle conflict without escalating; build trust over months; write clearly and unprompted (summaries, next steps, owners after calls); raise problems early including your own; push back on bad requirements
- Technical breadth as a generalist who has built software or data systems:
- Python: read others' scripts, follow logic, make modifications, understand stack traces
- SQL: write joins, aggregations, filters; diagnose slow queries
- Data pipelines and integration: APIs and failure modes (rate limits, pagination, retries, auth expiry), webhooks, CDC, file-based ingestion, batch vs. streaming trade-offs
- Cloud data platforms: working familiarity with at least one of Snowflake, Databricks, BigQuery, Redshift, or major cloud data warehouse
- Authentication: understand OAuth, SSO, service accounts, API keys; follow failing token exchange conversations
- Networking: DNS, firewalls, private connectivity, VPNs, SSH tunnels; know when network is the problem
- Containers and Kubernetes: conceptual understanding of containers, what Kubernetes does, pods, memory constraints (will not administer clusters)
- Linux: comfortable in terminal to read logs, check processes, run curl commands
**Nice to have:**
- Prior experience at data integration, ETL, or data platform vendor
- Background in data engineering or software engineering earlier in career
- Experience in regulated industry (healthcare, financial services, insurance)
- Entrepreneurial experience or fast-paced startup environment
