Software Engineer

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, briefly

You would work across the full stack of our SaaS platform: APIs, data models, background jobs, and integrations, writing production Python every day with FastAPI. Nobody hands you a detailed spec. You work with the team to figure out what needs to exist, build it, ship it, and keep it running. Small, focused team, and your work is visible from day one. The API team owns the centralized management plane for all operations on Nexla.

What you’ll own

• Platform features end to end. Data model, API, deployment, and the tests that keep them honest. Production Python every day, mostly FastAPI and SQLAlchemy.
• The APIs the platform runs on, internal services and customer-facing endpoints alike. Performance, reliability, and contracts other teams can build against without asking you first.
• The data layer underneath. Schemas, queries, and migrations across MySQL and Postgres, plus how those models change as the product does. Multi-tenancy and background processing sit here too.
• How your code behaves in production. Debug it, fix it, then build the monitoring and guardrails so that class of problem does not come back. You name the technical debt, performance bottlenecks, and security gaps without waiting to be asked.

Roughly 70% building and shipping, 20% architecture and paying down debt, 10% code review, documentation, and making the people around you better.

Thirty days in: Ship a PR first day, Refactor an existing module first week, Build a feature first month.

The hard problem

One thing we're working out right now is a common feature request we get from end-users is the need for dev/staging/production environments for Pipeline building. Building infrastructure for a physical environment deployment in the cloud is too much. Instead, we can build a world-class user experience of environments on our existing setup. The work here requires setting up the right data models in the database and lifecycle management on it with a slick UX.