Customer Engineer

Nexla, briefly

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, briefly 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.

• Technical resolution, end to end. Cases raised by customers and issues surfaced by our Ops and L1 team: first investigation, root cause, and the fix, including driving it with the engineering teams who own the code.

• The investigation itself. Logs, connectors, the Nexla environment, and whatever Python or shell you need to write to see what is actually happening. Written fast, often thrown away.

• What you learn, written down. Known and newly found solutions in the knowledge base so the next person does not start from zero, plus the Global Alerts and visibility updates that go to Sales and Customer Success when an issue reaches past one customer.

• The customer’s experience of all of it. Clear updates across email, tickets, Slack, and calls, and support through product releases.

The hard problem

Most of what lands on you will not be a bug in our code. It will be a third-party API that changed without telling anyone, a file that arrived malformed, an auth token that quietly expired, or a schema that moved. Across more than a thousand external systems, each with its own quirks, the hard part is working out which side of the boundary the problem sits on, and proving it, while the customer is waiting and reasonably assumes it is ours.

Must-haves

• 3 to 6 years in data engineering, technical support, or a closely related role.

• Strong Python. You write scripts quickly to debug something or set up an alert, and you are comfortable throwing them away afterwards.

• Solid SQL and hands-on with databases: Postgres, MySQL, Oracle, or SQL Server.

• REST APIs and the authentication around them: OAuth2, SSO, and token-based. You have debugged an auth failure that was not documented anywhere.

• Comfortable on both Linux and Windows, and with data formats including CSV, XML, JSON, and Parquet.

• You use AI tools in your actual work and can say specifically where they help and where they mislead you.

Bonus points

• Apache Airflow.

• You have worked a support or on-call rotation before and know what good looks like.

• Data integration, ETL, or iPaaS background.

You’ll thrive here if

• You can write a clear update to a frustrated customer and a clear one to a busy engineer, and you know those are different jobs.

• You like digging. Logs, traces, and the part where it finally makes sense.

• You do not handle a problem half-understood.

• You work well with people who are under pressure,