Business Area:
EngineeringSeniority Level:
Mid-Senior levelJob Description:
At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.
Team OverviewReady to take cloud innovation to the next level? Join Cloudera’s Anywhere Cloud (AWC) team and help deliver a true “build your own pipeline, bring your own engine” experience, enabling data and AI workloads to run anywhere, without friction or vendor lock-in.
We take the best of the public cloud—cost efficiency, scalability, elasticity, and agility—and extend it to wherever data lives: public clouds, private data centers, and even the edge. Powered by Kubernetes, our hybrid architecture separates compute and storage, giving customers maximum flexibility and optimized infrastructure usage.
As a Senior Software Engineer in Test, specializing in Performance and Scale, you will own the execution, automated validation, and continuous performance testing of our highly distributed, multi-cluster control plane. You will implement robust performance test suites and test tools to validate AWC both as an application installed via Taikun and as a platform that orchestrates other clusters. You will be responsible for defining and running localized performance test suites to evaluate Zero-Trust security models, API-first contracts, and cross-cluster resource management under heavy loads.
Performance Framework Implementation & Scaling: Build, scale, and maintain comprehensive automated performance test suites capable of validating the core control plane, cluster provisioning, and multi-cluster engine deployments.
Performance Baseline Ownership: Create and execute baseline test suites for measuring performance metrics and tracking regressions from release to release.
Data Layer & Metastore Scale Validation: Implement high-concurrency test configurations for containerized metadata and storage layers to ensure they meet strict enterprise targets for vast table limits, high operations per second, and low-latency read operations.
Profiling & Optimization Execution: Profile microservices performance, isolate infrastructure resource utilization bottlenecks (CPU, Memory, I/O), and work alongside backend core engineering to implement recommendations.
Mentorship & Best Practices: Mentor junior and mid-level test engineers (IC1/IC2) on distributed systems performance engineering best practices and automation standards.
Cross-Functional Execution: Collaborate closely with the Taikun (Kubernetes) and Foundational Services teams to integrate external components into the automated AWC performance test matrix.
Experience: 5+ years of professional experience as a Software Engineer in Test (SDET), Performance Engineer, or Quality Engineer within large-scale distributed systems or cloud-native environments.
Performance Engineering Tools: Hands-on expertise building load generators and leveraging industry-standard benchmarking tools (e.g., k6, JMeter, Gatling, Locust) to validate throughput, latency, and concurrency limits across REST/gRPC interfaces.
Kubernetes & Infrastructure: Strong proficiency with Kubernetes primitives, managing node/pod resource scaling limitations, and benchmarking storage IOPS profiles (e.g., Ceph, S3 object storage).
Programming Languages: Strong coding proficiency in Python, Go (Golang), or Java to build robust test scripts, extend automation harnesses, and interact directly with distributed product APIs.
Engine & Data Knowledge: Practical understanding of modern compute/streaming engine performance attributes (e.g., Spark tuning, Kafka throughput, Flink state backends) and open data lakehouse scale characteristics (e.g., Apache Iceberg).
Observability Stack: Solid experience utilizing observability stacks (Prometheus, Grafana, OpenTelemetry) to actively monitor p95/p99 latencies, analyze system metrics, and isolate memory leaks during soak and endurance runs.
AI Mindset: A proactive inclination towards adopting the latest AI productivity and AI-enabled test automation tools.
What you can expect from us:
Generous PTO Policy
Support work life balance with Unplugged Days
Flexible WFH Policy
Mental & Physical Wellness programs
Phone and Internet Reimbursement program
Access to Continued Career Development
Comprehensive Benefits and Competitive Packages
Paid Volunteer Time
Employee Resource Groups
EEO/VEVRAA
#LI_NK1
Skills Required
- 5+ years professional experience as a Software Engineer in Test (SDET), Performance Engineer, or Quality Engineer in large-scale distributed systems or cloud-native environments
- Hands-on expertise building load generators and using k6, JMeter, Gatling, or Locust to validate throughput, latency, and concurrency across REST/gRPC interfaces
- Strong proficiency with Kubernetes primitives, node/pod resource scaling, and benchmarking storage IOPS (e.g., Ceph, S3)
- Strong coding proficiency in Python, Go (Golang), or Java for building test scripts and extending automation harnesses
- Practical understanding of compute/streaming engine performance attributes (Spark tuning, Kafka throughput, Flink state backends) and data lakehouse scale characteristics (Apache Iceberg)
- Experience using observability stacks (Prometheus, Grafana, OpenTelemetry) to monitor p95/p99 latencies and analyze system metrics
- Experience profiling microservices performance and isolating infrastructure resource utilization bottlenecks (CPU, Memory, I/O)
- Experience creating and executing performance baseline test suites and tracking regressions across releases
- Mentorship experience guiding junior and mid-level test engineers on distributed systems performance engineering best practices
- Familiarity with Taikun or integrating Kubernetes-based platform components into automated performance test matrices
- Proactive inclination to adopt AI-enabled test automation and AI productivity tools
Cloudera Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cloudera and has not been reviewed or approved by Cloudera.
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Leave & Time Off Breadth — Unplugged Days provide recurring company‑wide extra days off in addition to generous PTO and holidays, creating predictable recharge time. Time away is a prominent part of the package and is consistently emphasized alongside regular leave options.
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Healthcare Strength — Comprehensive medical, dental, and vision coverage is paired with life and disability insurance, an EAP, and wellness programming. Added lifestyle support includes gym reimbursement in the U.S. and health checkups in some locations.
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Fair & Transparent Compensation — The company emphasizes equitable compensation and maintains a Fair Pay Workplace certification. Pay is described as competitive for many technical and go‑to‑market roles, aligning with this stance.
Cloudera Insights
What We Do
At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.
Why Work With Us
Impact at Scale: The infrastructure we build solves massive problems for top global banks, telecommunications giants, and healthcare providers. Cutting-Edge Tech: Work directly at the intersection of Open Source, Machine Learning, and Generative AI. Unmatched Flexibility: Enjoy a remote-friendly, hybrid culture that respects your time—including

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