Senior DevOps Engineer

Nexla is the leading Integration platform, built with AI, for AI. Nexla takes a metadata driven approach to converge diverse integrations across Data, Documents, Agents, Applications, and  APIs into a single design pattern. We accelerate the development of solutions for GenAI, Analytics, and Inter-company data. Nexla makes data users and developers up to 10x more productive by delivering a true blend of no-code, low-code, and pro-code interfaces.

Leading companies including DoorDash, LinkedIn, Johnson & Johnson, and LiveRamp trust Nexla for mission-critical data. Named in the 2022, 2023, and 2024 Gartner Magic Quadrant™ for Data Integration Tools and top-rated by customers on Gartner Peer Insights, headquartered in San Mateo, California.

At Nexla, our culture is built around our core values: Have Empathy, Be Curious, Be Intellectually Honest, Achieve Excellence, and Remember to Relax. We put our customers at the heart of everything we do, foster a data-driven mindset, take ownership of our work, and believe in the power of teamwork to achieve ambitious goals.

• →Streaming & Data Plane Reliability: Own the health of our Kafka-based runtime (managed via Strimzi on Kubernetes) - broker health, topic lifecycle and count management, partition and throughput tuning, certificate/secret rotation, and version upgrades - at a scale of hundreds of thousands of topics and hundreds of billions of rows per day.
• →Distributed Processing Engines: Operate and tune distributed system workloads in production in collaboration with backend teams, resource allocation, autoscaling, checkpointing, backpressure, and failure recovery for both batch and streaming jobs.
• →Stateful Services: Run Redis clusters and other stateful systems reliably - failover, persistence, liveness/readiness tuning, and capacity planning under heavy and bursty load.
• →Kubernetes & Operators: Take end-to-end ownership of Amazon EKS, Google GKE and the operators (Strimzi and others) running our stateful data workloads - cluster lifecycle, scaling, version upgrades, and resource governance.
• →Observability: Build deep, data-aware monitoring - consumer lag, throughput, partition skew, job latency, error rates - not just host and CPU metrics. Make the data plane's behavior legible before it breaks.
• →Incident Management: Lead root-cause analysis for distributed-systems failures (broker outages, crashloops, sink decommissions, control-plane race conditions) and drive durable fixes. Mitigate fast, but design out the recurrence.
• →Infrastructure as Code & Automation: Provision and manage cloud infrastructure with Terraform; build operational runbooks and automation, including for air-gapped / private enterprise installs (pre-staged images, operator-facing procedures).
• →Collaboration: Partner with platform, runtime, and connector engineering - and with SREs and support - to ship and scale new data-movement features reliably in a large-scale Linux environment.r with SREs, L2/Support, and developers to deploy and scale new product features and improve production monitoring in a large-scale Linux environment.

• Experience: 8+ years in infrastructure, SRE, or DevOps, with significant time spent operating production distributed data systems (not just application/cloud infra).
• Kafka: Deep, hands-on operational experience running Kafka at scale in production - ideally on Kubernetes via Strimzi - including upgrades, topic/partition management, performance tuning, and TLS/secret rotation.
• Distributed Processing (Strong Plus): Production experience operating one or more of Spark, Flink, or Ray - resource tuning, checkpointing, failure recovery.
• Stateful Systems (Must Have): Production experience with Redis (clustering, persistence, failover) and a solid understanding of operating stateful workloads on Kubernetes (StatefulSets, PVCs, probes, operators).
• Data Warehouses: Familiarity operating against Snowflake, BigQuery, or similar, and an understanding of JDBC connectivity and sink reliability.
• Kubernetes & EKS: Strong hands-on EKS - cluster creation, scaling, version upgrades, and operator management.
• Infrastructure as Code: Advanced proficiency with Terraform.
• Programming: Proficiency in Python (or similar) for automation and tooling. Comfort reading and debugging JVM-based systems is a strong plus.
• Reliability Mindset: Demonstrated ownership of incident management, RCA, capacity planning, and performance tuning for high-throughput systems.
• CI/CD: Solid understanding of CI/CD methodology (Jenkins, GitHub Actions, or GitLab CI) for containerized and non-containerized apps. Supporting, not the core of the role.
• Nice to Have: Configuration management (Ansible preferred); broader AWS services (IAM, VPC, EC2, S3, Lambda); AWS CloudFormation.
• Soft Skills: Excellent communication and organizational skills; ability to coordinate effectively within a team and with customers.

• You own the hard part. The stateful, distributed systems that move billions of rows are the platform's most demanding reliability problems - and they'd be yours.
• Impact at scale from day one. Your work keeps mission-critical data flowing for companies like DoorDash and LinkedIn.
• The AI wave is real for us. We're not bolting AI onto a legacy product. Intelligent connectors, context-aware data movement, and agentic workflows are the core of what we're building next - on top of the runtime you'd run.
• Small team, big problems. Direct access to the CTO, real influence over product direction, and the autonomy to make significant technical bets.
• Recognized platform, startup energy. Enterprise validation with the speed and ownership of an early-stage company.