Software Engineer, API(Python)

About Nexla Nexla is the leading Integration platform, built with AI, for AI. Nexla takes a metadata driven approach to converge diverse integrations across Data, Documents, Agents, Applications, and APIs into a single design pattern. We accelerate the development of solutions for GenAI, Analytics, and Inter-company data. Nexla makes data users and developers up to 10x more productive by delivering a true blend of no-code, low-code, and pro-code interfaces.

Leading companies including DoorDash, LinkedIn, Johnson & Johnson, and LiveRamp trust Nexla for mission-critical data. Named in the 2022, 2023, and 2024 Gartner Magic Quadrant™ for Data Integration Tools and top-rated by customers on Gartner Peer Insights, headquartered in San Mateo, California.

At Nexla, our culture is built around our core values: Have Empathy , Be Curious , Be Intellectually Honest , Achieve Excellence , and Remember to Relax . We put our customers at the heart of everything we do, foster a data-driven mindset, take ownership of our work, and believe in the power of teamwork to achieve ambitious goals.

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 Example of a new feature you will build: One 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.

Must-haves

• 7+ years of professional software engineering, with Python as your primary language. This is a Python role, not a backend role where Python happens to be in the stack.
• FastAPI, or a similar async Python framework such as Starlette, plus SQLAlchemy. You understand async patterns, dependency injection, and ORM tradeoffs.
• Strong SQL and relational database skills. You can design a normalized schema, write a performant query, and reason about indexing and query plans.
• You write unit and integration tests, and you have opinions about what is worth testing.
• You have owned production systems and know what good operational discipline looks like: monitoring, alerting, incident response, post-mortems.
• You use AI tools in your actual work and can say specifically where they help and where they mislead you.

Bonus points

• Working knowledge of AWS or GCP. You have deployed applications, used managed services, and debugged problems below the application layer.
• Background job processing (Celery, Dramatiq, or similar), or message queues and event-driven architectures (Kafka, RabbitMQ, Redis Streams).
• Data-intensive systems, or the data integration domain.

You’ll thrive here if

• You own what you build. You do not wait for permission, and you do not need a finished spec to start.
• You are comfortab…