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At a glance
- Education
- Master's
Skills mentioned
About the role
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.
About the role
You’ll own the technical relationship with our accounts: onboarding, implementation, adoption, escalations, and everything that happens when a pipeline that used to work stops working.
You are the person the customer's data team calls, and the person who tells them the truth when the answer is inconvenient. Most of the job is conversation: understanding what they are actually trying to do, explaining what the platform will and will not do for them, keeping their expectations aligned with reality, and raising problems while they are still small.
The technical part matters, but not in the way people usually assume. We want a generalist who has built things before and therefore understands how the pieces fit together: what a Kubernetes cluster is for, why a VPN tunnel might be the bottleneck, what a stack trace is telling you, what a slow query looks like. You will not be asked to be the deepest engineer in the room. You will be asked to be the person who can talk to the deepest engineer in the room, understand them, and translate what they said to a VP who has thirty seconds.
What you will do
- Own the technical relationship for a portfolio of accounts. You are the continuity: the person who knows their environment, their history, and their constraints.
- Lead onboarding and implementation. Understand what the customer is trying to build, design the integration with them, and guide it to production.
- Run the hard conversations. A missed date, a degraded pipeline, a limitation they did not expect, a customer who is upset and technically wrong. You stay calm, keep credibility, and land on a next step everyone agrees to.
- Flag risk early and in writing. Adoption stalling, a champion who left, a project quietly losing sponsorship, an architecture heading somewhere that will not scale. Flagging a risk three weeks early is worth more here than fixing it heroically after it lands.
- Translate between audiences. The same situation explained to a data engineer, to a security reviewer, and to a VP, without losing any of them.
- Troubleshoot alongside the customer: failed runs, schema drift, connector and authentication failures, connectivity problems, performance issues. You do the first-line diagnosis and bring engineering a clear reproduction when it needs to escalate.
- Partner with CSM and Support on renewal risk, and hand expansion signals to Sales.
- Mentor other SAs on how to handle customers, not just how to solve problems.
What you need
Communication and customer ownership (the core of the role)
- You can explain a technical concept to a non-technical stakeholder without either dumbing it down or losing them.
- You handle conflict without escalating it. You can tell a customer no, or "that was our mistake" and keep the relationship intact.
- You build trust over months, not meetings. Customers should ask for you by name.
- You write clearly and unprompted: summaries, next steps, and owners after every meaningful call.
- You raise problems early, including your own. We would much rather hear "this is slipping and here is why" than a confident status update that turns out to be wrong.
- You push back on bad requirements instead of silently building them.
Technical breadth
You should be a generalist who has built software or data systems at some point. Not necessarily an expert in any one of these, but genuinely conversant in most of them, in the way someone is conversant after having actually worked with them:
- Python. You can read someone else's script, follow what it does, make a modification, and understand stack traces.
- SQL. Comfortable writing joins, aggregations, and filters. You can look at a slow query and have a sensible first guess about why.
- Data pipelines and integration. APIs and their common failure modes (rate limits, pagination, retries, auth expiry), webhooks, CDC, file-based ingestion, batch versus streaming trade-offs.
- Cloud data platforms. Working familiarity with at least one of Snowflake, Databricks, BigQuery, Redshift, or a major cloud data warehouse.
- Authentication. You understand what OAuth, SSO, service accounts, and API keys are and roughly how they work. You can follow a conversation about a failing token exchange.
- Networking. DNS, firewalls, private connectivity, VPNs and SSH tunnels. You do not need to configure them, but you need to understand the concepts well enough to hold a productive call with a customer's network team and know when the network is the problem rather than the platform.
- Containers and Kubernetes. Conceptual understanding only: what a container is, what Kubernetes does, why workloads run in pods, what it means when something runs out of memory. You will not be administering clusters.
- Linux. Comfortable enough in a terminal to read a log file, check a process, or run a curl command.
Experience
- 5+ years in technical customer-facing work: solutions architecture, implementation, technical account management, professional services, consulting, support engineering, or a data engineering role that involved heavy stakeholder contact.
- At least some of that spent hands-on building, not only advising.
- Experience owning accounts or projects through delivery, not just handing off.
Nice to have
- Prior experience at a data integration, ETL, or data platform vendor.
- Background in data engineering or software engineering earlier in your career.
- Experience in a regulated industry (healthcare, financial services, insurance).
- Entrepreneurial experience or similar (having worked at a fast-paced startup environment).
What this role is not
- Not a support queue. You own outcomes on named accounts.
- Not a deep engineering role.
- Not a pure relationship role either. You need to be technical enough to earn the customer's respect in the first meeting.
Interview process
- Recruiter screen (30 min)
- Hiring manager conversation (45 min)
- Technical breadth session (60 min): troubleshooting reasoning, plus basic Python and SQL
- Customer scenario (60 min): you handle a difficult account situation with a panel playing customer roles
Why Nexla? Why now?
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.
Originally posted on Himalayas