The Role
Lead design and execution of load and stress tests, build and operate load-testing infrastructure and automation, analyze and fix performance bottlenecks across backend, DB, messaging, and networking, integrate performance checks into CI/CD, and communicate technical results and business impact to stakeholders.
Summary Generated by Built In
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.
Key Responsibilities
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:
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:
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 automation performance testing and analysis Load as a Service for product teams: traffic generators, load managers, mocks, test stubs, data feeders, and helper services used across partner integrations. Building load test scenarios to Jmeter (DSL framework) and k6 in JavaScript. Innovate on tooling to make load tests easier to create, run, and analyze for the whole team.
CI/CD Integration:
Integrate performance tests into our CI/CD pipeline as QG. Automate load tests run to every new integration or code change is evaluated for performance regressions. Ensure that build pipelines can catch and flag performance drops (e.g. increased response times, higher resource usage) before changes reach production.
Reporting&Communication:
Prepare executive-ready performance reports for engineering teams and C-level stakeholders. Highlight key metrics (p95/p99 latency, throughput, error rates, saturation signals) and identify primary scalability bottlenecks across services, databases, caches, and messaging systems, and compare results against SLOs and capacity targets. Provide clear root-cause explanations and actionable recommendations, including performance vs cost trade-offs, to support production readiness and Go/No Go decisions.
Performance Culture:
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 automation performance testing and analysis Load as a Service for product teams: traffic generators, load managers, mocks, test stubs, data feeders, and helper services used across partner integrations. Building load test scenarios to Jmeter (DSL framework) and k6 in JavaScript. Innovate on tooling to make load tests easier to create, run, and analyze for the whole team.
CI/CD Integration:
Integrate performance tests into our CI/CD pipeline as QG. Automate load tests run to every new integration or code change is evaluated for performance regressions. Ensure that build pipelines can catch and flag performance drops (e.g. increased response times, higher resource usage) before changes reach production.
Reporting&Communication:
Prepare executive-ready performance reports for engineering teams and C-level stakeholders. Highlight key metrics (p95/p99 latency, throughput, error rates, saturation signals) and identify primary scalability bottlenecks across services, databases, caches, and messaging systems, and compare results against SLOs and capacity targets. Provide clear root-cause explanations and actionable recommendations, including performance vs cost trade-offs, to support production readiness and Go/No Go decisions.
Performance Culture:
- Champion a culture where performance = product quality.
- Educate and influence teams to treat performance testing as an integral, continuous part of the development lifecycle rather than a one-time task.
- Share best practices and success stories, ensuring that performance considerations are baked into design, coding, and testing processes from the start.
- Conducting production load and traffic validation experiments with full ownership of risk assessment, blast radius control, observability, and rollback procedures.
- Work with geo-distributed, high-load systems (200k+ RPS) and latency-sensitive mobile flows
- Set up production-like performance environments in AWS
- Conducting and modeling load tests in production
- Performance analytics → hypotheses → experiments → fixes → measurable business impact (latency, conversion, GMV)
Skills, Knowledge and Expertise
- 5+ years in Performance/SRE/Load Testing
- Strong programming skills in Golang/Kotlin/Java
- Strong k6 or JMeter DSL - Performance Testing Mastery: Hands-on expertise with modern load testing tools, especially k6 and/or JMeter (and their scripting DSL). Deep understanding of performance best practice – you know when to apply load testing vs stress testing vs soak testing, and how to interpret the results of each.
- Hands-on experience with AWS infrastructure on for high-load systems: EKS, EC2, Auto Scaling Groups, ALB/NLB, VPC, subnets, routing, security groups
- Designing and tuning autoscaling strategies for compute (KEDA/Karpenter/Cluster Autoscaler, EC2 scaling policies)
- Understanding network performance characteristics: latency, cross-AZ traffic, NAT/egress limits, load balancer behavior under high RPS
- Profiling Golang, Kafka, Redis, DB tuning experience
- Automation loadtesting and make QG (JVM languages and Go)
- SRE mindset (RED/USE, SLO, error budgets)
Why join us
- Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries.
- Develop your professional skills with access to mentoring, career consulting, and learning programs.
- Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
- Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
- Work alongside people who take initiative, speak openly, and challenge themselves to grow.
- Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
About
inDrive is a global tech company on a mission to challenge injustice. We started in 2012 in the coldest city on Earth, when a group of friends created a way for people to agree on fair ride prices. That idea grew into one of the world’s top ride-hailing apps, now with 360M installs across 48 countries.Today, we offer more than rides: from freight and delivery to intercity travel and financial services, all designed to put people first. Our goal is to positively impact 1 billion lives by 2030. Through inVision, our non-profit arm, we support education, entrepreneurship, and equality in underserved communities.Ready to ignite your inner drive?
Skills Required
- 5+ years in Performance/SRE/Load Testing
- Strong programming skills in Golang, Kotlin, Java
- Strong k6 or JMeter (DSL) expertise and scripting (k6 JavaScript, JMeter DSL)
- Hands-on AWS experience for high-load systems (EKS, EC2, Auto Scaling Groups, ALB/NLB, VPC, subnets, routing, security groups)
- Designing and tuning autoscaling strategies (KEDA, Karpenter, Cluster Autoscaler, EC2 scaling policies)
- Experience with Kafka, Redis, and DB tuning (Aurora MySQL/RDS)
- Profiling and troubleshooting backend issues (Golang profiling, GC, CPU/memory hotspots, thread pools)
- Build internal performance tooling in Golang and automation (Load as a Service, traffic generators, mocks, data feeders)
- Integrate performance tests into CI/CD pipelines as quality gates
- Observability and monitoring experience (Grafana, Prometheus, New Relic, Dynatrace, Zabbix, ELK Stack, Coroot)
- SRE mindset and practices (RED/USE, SLOs, error budgets, capacity planning)
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The Company
What We Do
inDrive is a global IT and transportation platform inDrive is one of the world’s fastest growing online ride-hailing services. Its services are available in over 749 cities in 46 countries throughout the world. The Company’s app has been downloaded over 150 million times. inDrive offers other services, including intercity transportation, freight and cargo services, as well as delivery services in different markets of operations. inDrive is based in Mountain View, California, and operates regional hubs in the Americas, Asia, the Middle East, Africa and the countries of the CIS, and employs over 2,000 people. In early 2021, inDrive achieved unicorn status after closing a $140m investment round with Insight Partners, General Catalyst, and Bond Capital, which valued the company at $1.23 billion.
