Sr. Manager, Quality Engineering

Netskope•Santa Clara, CA

19h•$182,500 - $260,500•Remote

About The Position

Netskope is a leading cloud security company that redefines cloud, network, and data security. Founded in 2012, Netskope has built a market-leading platform and an award-winning culture. The Data Engineering team at Netskope is at the forefront of security, big data, and cloud computing, processing nearly 1PB of data daily with high uptime to deliver real-time security insights. This role is for a seasoned manager to lead the Quality organization, focusing on modern testing strategies and AI-augmented testing within a large-scale data infrastructure. The quality engineers are embedded within development squads, emphasizing a shift-left approach and shared ownership of quality. Success is measured by engineering metrics like deployment frequency and MTTR, rather than traditional bug counts. The role also involves leading a platform tooling team to build shared CI/CD gates, contract-testing infrastructure, and chaos templates to enable faster, independent team movement while setting technical standards and career paths.

Requirements

4+ years of management experience leading distributed teams of 10+ people.
A track record of building modern testing strategies and automation frameworks.
Deep experience with data systems like Postgres, Clickhouse, Trino, Spark, Iceberg, Kinesis, Kafka, and cloud-native architectures.
Strong familiarity with AI-driven tooling, including evaluating AI-generated tests and code quality.
Great communication skills—comfortable influencing others and explaining quality goals to both technical and non-technical individuals.
BSCS degree or equivalent required.

Nice To Haves

Experience with contract/consumer-driven testing or chaos engineering is a big plus.
Prior hands-on experience in a Senior IC role is preferred.
MSCS or equivalent strongly preferred.

Responsibilities

Lead and mentor 10+ engineers, fostering a culture of shared ownership.
Define quality strategy and standards using DORA metrics (like deployment frequency and MTTR) rather than legacy QA metrics.
Guide teams in designing automation frameworks, contract-based testing, and resilience strategies for complex systems.
Oversee the rollout of AI-augmented testing, including LLM-driven test generation and evaluation frameworks for AI products.
Establish standards for data integrity and system reliability, even when things fail at scale.
Work closely with Engineering, Product, and Program Management to ensure quality is built into the architecture from the start.
Assist with production deployments and lead root-cause analysis when issues arise.
Act as a passionate champion for quality and customer success across the organization, building trust through influence.