Senior Performance Engineer

The QA Load team is responsible for the performance and scalability of services across their entire lifecycle — from Go/No-Go decisions before launch to working with real production traffic. We design and execute load tests, model real-world usage scenarios, identify and eliminate bottlenecks in backend systems, databases, messaging, and infrastructure, and work closely with SRE, DevOps, and product engineering teams.
The role focuses on systematic performance improvement: building load-testing infrastructure, automation and CI/CD quality gates, performance analysis and experimentation, and translating technical metrics (latency, RPS, p95/p99) into clear business impact. Our goal is to make performance a core aspect of product quality and ensure the stable operation of high-load, geo-distributed systems.

Performance Audits & Integration Support: Go/No-Go decisions with our stakeholder about launches services before integrating them into our ecosystem and integrations support (audit APIs, web, databases, caches, message brokers) Design&Execute Load Tests global strategy:
Modeling of load testing scenarios (load, stress, soak, peak, find max tests), methodology and formation of non-functional requirements for system using k6, JMeter (DSL framework)
Simulate real-world scenarios and traffic patterns to validate systems under production-like conditions (including high RPS, concurrency levels, data skew, and geo-distributed load)
Load Modeling:
Calculate and validate detailed load models – e.g. expected RPS, concurrent users, transaction volumes, and data distribution.
Adjust these models to mirror peak and edge-case scenarios, ensuring our capacity planning is grounded in reality.
Bottleneck Analysis & Fixes: Find, analyze, and fix performance bottlenecks. This includes:
backend issues (CPU hotspots, memory leaks, GC pauses, thread pool exhaustion)
DB inefficiencies (Aurora MySQL/RDS indexing, query locks, IOPS limits)
messaging/queue back-pressure (Kafka partitioning, consumer lag, throughput limits)
network constraints (latency, load balancer settings, timeouts)
Work hands-on with SRE, DevOps, and Backend teams to implement optimizations and verify the improvements. Prepare for Production Traffic: Ensure services are production-ready. Drive capacity planning exercises and stress tests to determine scaling lag/limits and failure modes. Verify that services can gracefully handle traffic spikes or component failures without violating SLAs or SLOs. Help partners configure autoscaling policies for sustained performance Loadtesting Infrastructure Setup&Tuning: Set up, audit, and operate performance testing infrastructure for our services load testing automation and geo-distributed load generator to simulate production traffic Performance Analysis & Experimentation:
Approach performance issues with an performance analyst/architect mindset – form hypotheses, design experiments, run tests, and draw conclusions to configuration infrastructure and fix backend code.
For example, if latency is high, hypothesize the cause (e.g. database index missing), test the hypothesis, and then make a the fix. Repeat this iterative process to methodically improve system performance.
Business Impact Translation: Translate technical performance insights into business risks and opportunities. For instance, demonstrate how reducing latency or improving p95 response time can increase conversion rates or GMV. Communicate these insights to both engineers and non-technical stakeholders to underline the business value of performance work. Observability & Monitoring: Utilize observability tools to monitor performance during tests. Set up dashboards and alerts in Grafana/Prometheus and other systems (e.g. New Relic, Dynatrace, Zabbix, ELK Stack, Coroot), analyze traces and logs. Use few methods to interpret results and guide troubleshooting. Performance Tooling: Build internal performance tooling in Golang: Design and maintain internal tools for aut