Senior Site Reliability Engineer

Role
You will own the reliability of the distributed data systems at the heart of Nexla - the streaming runtime and processing engines that move hundreds of billions of rows per day for top-tier enterprises. This is an SRE role for our big data stack: Kafka, Spark, Flink, Ray, Redis, and data warehouses, all running on Kubernetes.

This is not a cloud-provisioning role. We are looking for someone who has lived inside stateful, high-throughput systems in production who has chased down a broker outage, a checkpoint stall, a crashlooping cache, and a sink that silently stopped writing, and who fixes the architecture rather than the symptom. If keeping a large, busy data platform alive and fast is the kind of problem you find satisfying, you will have a lot of fun working with us. This is a unique opportunity to shape the foundation of a product that is defining the next wave of intelligent, context-aware data movement.

Responsibilities

• 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.

Qualifications

• 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.

Why This Might Be Worth It

• 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.

Location
Pune(preferred) or Bengaluru