Software Engineer, API(Python)

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.

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.

• 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.

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.

• 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.

• 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 own what you build. You do not wait for permission, and you do not need a finished spec to start.
• You are comfortable defining scope on the fly and shipping before the full picture is clear.
• You would rather fix the class of problem than the instance of it.
• You leave behind enough context and documentation that the team can operate what you built without you in the room.
• You can explain a technical tradeoff to product or leadership without oversimplifying it.

One thing to be clear about: our product definition is still moving as AI reshapes data integration, and the platform has to move with it. If you want a stable, well-defined surface to work on, this is not it.

4 conversations, about 2 weeks end to end.

• Coding
• API design
• API design
• Hiring Manager

On AI in the (coding round): Use it the way you would on the job, ours or anyone else’s. We build AI tooling and we expect you to use AI tooling, so watching you work without it would tell us nothing useful. What we dig into is your judgment: what you delegated, what you verified, and what you threw away.

You will hear from us either way.

Bengaluru, hybrid, 2 days a week in office. Compensation includes base salary and equity, set by depth and experience rather than by title. You will need to overlap with morning Pacific hours for syncs, design reviews, and collaboration with our US-based leadership and engineering teams.

• What we are building now. MCP Studio and Express, our two newest bets.
• Who uses us, and why. nexla.com/customers
• The product in engineer terms. docs.nexla.com
• Our CEO on why we take "Remember to Relax" seriously. Read the founder story
• Everything else. nexla.com  ·  Blog  ·  All open roles

A few large companies - DoorDash, SentinelOne, Johnson & Johnson, LinkedIn, Amex, Integrity Marketing, among them use Nexla for data integration. Connectors, runtime, transformations, scheduling, the parts of the stack where data has to move between systems reliably.

The reason this is an interesting moment to join is what the agent shift is doing to the category. Data integration used to mean "land this data in that warehouse so a human can look at it." That product is mature. What it's becoming is closer to "an agent asks a business question, and the platform figures out which data and what code and which APIs add up to a real answer." That's a much bigger problem, and most of the architecture for it hasn't been built yet by anyone.

A concrete example. A revenue team wants to ask "which enterprise customers are showing renewal risk" and get a real answer through whatever agent or app they work in. Today there is no clean way to answer that question, it requires CRM, usage, support, and billing data joined in org-specific ways, and the calling agent can't just invoke a tool that returns the right answer unless someone first establishes whether the underlying data is even capable of producing one.

That is the system we are building. A probe agent investigates the data plane whether the right fields are populated, whether freshness is adequate, whether the joins exist, whether credentials cover the required scope and returns a grounded feasibility answer. Where there are gaps, Express.dev composes the pipelines to close them. The capability is then exposed as an MCP server: "renewal risk" as a curated product, with the business logic correct and the query semantics described well enough that the calling agent uses it as intended. The MCP Gateway governs which agents can call which capabilities, with the policies, audit, and observability an enterprise control plane requires.

The bet is that ten years of connector work, an enterprise customer base, and a runtime that already moves real volume are the right foundation to define this layer from.