Vacancy description
Nexla
India, Bengaluru
Software Engineer (Python/SQL) in Bengaluru, India is listed on Jobeax. Browse 30,000+ vacancies available.
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.
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.
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. You work with the team to figure out what needs to exist, build it, ship it, and keep it running. The API team owns the centralized management plane for all operations on Nexla.
Data model, API, deployment, and the tests that keep them honest. Production Python every day, mostly FastAPI and SQLAlchemy.
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. 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.
Bonus points
Working knowledge of AWS or GCP. Data-intensive systems, or the data integration domain.
You are comfortable defining scope on the fly and shipping before the full picture is clear.
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. 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. 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. We are standing at the precipice of the GenAI revolution, but the biggest bottleneck isn't the models, it's the data. you are stepping into the critical layer of the modern data stack that powers the AI economy. We are the Data Fabric that enables industry titans like LinkedIn, DoorDash, and J&J to turn messy, siloed data into ready-to-use products for RAG and predictive models. This is your opportunity to move beyond simple tooling and build the actual infrastructure that democratizes data access for the next decade of innovation. If you want to solve the hardest problems in data engineering and own a piece of a market projected to hit billions, your career belongs here.
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