Performance Engineer -- Non Functional QE

Cloudera•San Jose, CA

1d•$124,000 - $155,000•Hybrid

About The Position

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. At Cloudera, our Data Services Pillar is the heart of data innovation. We don’t just work with technology; we build it. Our mission is to empower data practitioners by creating seamless, enterprise-grade experiences for data engineering, warehousing, streaming, operational databases, and AI. You will be a key member of the NFQE (Non Functional QE) team that drives the performance reliability of Cloudera’s Kubernetes‑hosted data services. The role blends deep technical knowledge of performance testing, distributed data workloads, and container orchestration with a data‑driven mindset. You’ll design, automate, run, and analyze performance tests for Cloudera’s flagship services, ensuring they meet or exceed customer‑defined SLOs/SLAs at scales.

Requirements

3 + years of industry experience in performance-related work, ideally on large-scale distributed systems
Understanding of DBMS algorithms and data structure fundamentals.
Understanding of hardware trends and full-stack systems performance: CPU, RAM, storage, network, Linux kernel, JVM, and distributed systems performance.
Understanding of performance analysis tools and techniques.
Strong design, coding skills, and test automation skills (Java/C++/Golang/Python preferred)
Knowledge of relevant frameworks, cloud provider knowledge, K8s, etc.
Ability to work in a distributed setting with team members spread in multiple geographies
Demonstrated ability to work on large cross-functional projects, including strong written communication skills and a collaborative mindset, as you will be working with many teams inside and outside of Cloudera.
Experience with benchmark and performance test design. You eshould understand basic concepts of performance testing including different types of performance tests (microbenchmarks, end-to-end benchmarks, concurrency and scale testing), how to reduce (or deal with) noise in test results, etc.
Experience designing performance tests that provide useful insights into specific aspects of performance.
Solid understanding of basic performance theory - in particular a very good understanding of latency, throughput, and concurrency and how they relate to each other.
Strong understanding of the types of workloads they'll be testing
Ideally they should have specific experience creating performance tests for the specific product area they'll be working on (SQL, ML, etc).
B.S. or M.S. in Computer Science or equivalent experience.

Nice To Haves

Experience with the Hadoop ecosystem (i.e. Hive, Impala, Spark), in specific
Prior work on large‑scale data lakehouse or data‑warehouse performance
Hands-on experience with containerization, Kubernetes, public cloud infrastructure (AWS, Azure and/or GCP) and mesh-networks
Certifications: CKA/CKAD, AWS Solutions Architect, GCP Cloud Architect, Azure Solutions Architect, or equivalent.
Experience writing performance tests that also verify data‑privacy and audit compliance (e.g., GDPR, HIPAA).

Responsibilities

Work with internal development teams and the open source community to proactively drive performance improvements/optimizations across our data warehouse and Data Engineering stack.
Work with product managers, developers and the field team to understand performance and scale requirements, and develop benchmarks based on these requirements.
Develop automation to execute benchmarks, collect and aggregate metrics and profiles, and report results, trends, and regressions.
Analyze performance and scalability characteristics to identify bottlenecks in large-scale distributed systems.
Perform root cause analysis of performance issues identified by internal testing and from customers and suggest corrective actions.
Evaluate performance of systems and provide related guidance to the team.