Senior DevOps Engineer, Data Platform

Nexla, briefly

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.

Role, briefly Our data plane moves hundreds of billions of rows a day across hundreds of thousands of Kafka topics, with Spark, Flink, Ray, and Redis running on EKS and GKE. You would own its reliability as an engineering problem rather than an operations one: the SLOs that define what working means, the instrumentation that proves it, and the software you write to remove the manual work in between. This is not a cloud-provisioning or CI/CD role. It is Senior level, so your influence is expected to reach past the systems you personally touch.

What you’ll own

• What reliable means, in numbers. Availability, freshness, and latency SLOs that platform and runtime teams actually agree to, error budgets that govern release pace, and the data-aware instrumentation behind them: consumer lag, throughput, partition skew, job latency, error rates. Not host and CPU metrics.

• The stateful data plane itself. Kafka via Strimzi at hundreds of thousands of topics, Spark, Flink, and Ray workloads, Redis clusters, and the EKS and GKE clusters underneath. Broker health, checkpointing, backpressure, failover, upgrades, capacity.

• Toil, treated as an engineering problem. Measure the manual work the team does to keep things running, then write software to remove it. Terraform-provisioned infrastructure, runbooks, and automation, including for air-gapped and private enterprise installs.

• Incidents and what comes after. Lead root-cause analysis on distributed-systems failures, run blameless postmortems, and drive the durable fix. Mitigate fast, then design out the recurrence.

Senior scope: you set the reliability bar other engineers design against. Changing how teams outside SRE build is part of the job, not a side effect of it.

The hard problem The failures we hate are the silent ones: a sink that stopped writing and told nobody, a consumer group quietly drifting behind, a checkpoint that stalled. At hundreds of thousands of topics, host and CPU metrics tell you nothing about whether data is actually moving. Making the data plane’s behaviour legible before it breaks is the standing problem on this team.

Must-haves

• 8+ years in SRE or infrastructure engineering, most of it operating production distributed data systems rather than application or cloud infrastructure.

• Deep hands-on Kafka at scale in production, ideally on Kubernetes via Strimzi: upgrades, topic and partition management, performance tuning, and TLS and secret rotation.

• Redis and stateful workloads on Kubernetes in production: StatefulSets, PVCs, probes, operators. Plus strong hands-on EKS, including cluster lifecycle and version upgrades.

• You write software, not just scripts. Python or similar, advanced Terraform, and enough range to build tooling other engineers depend on.

• You have owned SLOs, error budgets, or an equivalent reliability contract, and you can describe a time one of them changed what a team shipped.

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

Bonus points

• Production experience operating Spark, Flink, or Ray: resource tuning, checkpointing, failure recovery.

• Operating against Snowflake, BigQuery, or similar, including JDBC connectivity and sink reliability.

• Comfort reading and debugging JVM-based systems.

• GKE alongside EKS, Ansible, or broader AWS services such as IAM, VPC, EC2, S3, and Lambda.

You’ll thrive here if

• You fix the architecture, not the symptom.

• You would rather delete a class of alerts than get faster at answering it.

• You can hold a reliability line with a team that wants to ship, and keep the relationship afterwards.

• Keeping a large, busy data platform alive and fast is the kind of problem you actually enjoy.

One thing to be clear about: this is a hands-on Senior role on a small team. You set the standards and you also do the work. There is no SRE organisation underneath you.

How we hire 3 conversations, about 2 weeks end to end.

• System Design - 60 mins

• K8s and Infra - 60 mins

• Leadership - 60 mins

• Spillovers(if any) - 45 mins

The practical stuff Bengaluru, hybrid(2 days). Compensation includes base salary and equity, set by depth and experience rather than by title. Pacific time-zone overlap for leadership meetings and cros-team calls. Weekend deployment twice a year.

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.