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.
🏢 About Nexla
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.
🎯 The Role
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.
✅ Key Responsibilities
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.
📌 Required Qualifications
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.
⭐ Desirable Experience
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.
🎁 Benefits
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.
