Customer Engineer
Nexla
Customer Engineer
Nexla
No-code data integration for AI
Nexla is the data connectivity layer for enterprise AI. Over 1,000 bidirectional connectors give agents real-time access to data and actions over MCP. Nexla's Agentic Data Integration handles high-volume streaming ETL, CDC, and Document ETL. At the core of the platform is Helix, a context engine that continuously learns from metadata, schemas, telemetry, and documentation to give agents accurate context and keep pipelines reliable. Nexla processes over a trillion records a month for enterprises including DoorDash, LiveRamp, LinkedIn, and J&J. Honorable Mention in the Gartner Magic Quadrant™ for Data Integration Tools, and top-rated by customers on Gartner Peer Insights four years running.
Role
You would be the technical bridge between our customers and our engineering teams. When a customer’s pipeline breaks, or our Ops team spots something before the customer does, you take it: dig through logs and connectors, find the actual cause, and see it through to a fix. This is not ticket triage. You own the resolution, including the engineering conversations it takes to get there. Python, SQL, and REST APIs are the tools you reach for daily.
What you’ll own
Investigate and resolve
Support customers
Share knowledge and keep teams informed
Skills required
You’ll thrive here if
You're curious about new tech stacks and love unpacking things — understanding code and how machines work.
This role is an exciting opportunity for someone passionate about customer support who thrives in a dynamic environment and is eager to grow their technical skills. If you love problem-solving and are committed to delivering exceptional customer experiences, we'd love to hear from you!
The practical stuff
Bengaluru preferred, and we are open to candidates working remotely elsewhere in India. The role runs on a rotational shift pattern with weekend coverage shared across the team. We do not run late-night shifts. Compensation includes base salary, bonus, and equity, set by depth and experience rather than by title.
Worth a look before you apply
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.
Nexla provides an enterprise-grade AI-powered data integration platform that turns data from any source into production-ready data products for AI applications, using a no-code/low-code interface. It unifies data integration, preparation, governance, and monitoring with 700+ pre-built connectors and supports ELT, ETL, streaming, APIs, and Retrieval-Augmented Generation, including Nexsets—virtual data products with semantic metadata, data quality checks, and lineage. The platform emphasizes automated, governed data layers for AI agents, offering end-to-end data governance and collaboration across technical and non-technical users. Its goal is to reduce data deployment times from months to days by turning raw data into ready-to-use data products for AI agents and applications.
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