Senior SDET / QE Engineer

The role

Level: Senior (5–8 years)
Team: Engineering
Type: Contract

RiseServ’s partner company is hiring a Senior SDET to own test automation across our data security platform. You will build the frameworks that prove our product works across cloud data stores, SaaS connectors, APIs and UI, at the scale our customers run it. This is a hands-on engineering role: you write production-quality test code, shape CI/CD quality gates, and decide what “ready to ship” means.

About RiseServ’s partner company

RiseServ’s partner company is an AI-native data security, governance and management platform, backed by a $25M Series A led by Greylock and a strategic investment from Snowflake Ventures (BusinessWire, CB Insights). Our Metadata Lake is a continuously updated graph that discovers, classifies and contextualizes data across IaaS, PaaS, SaaS and AI systems, at petabyte scale and without moving data out of customer boundaries.

Enterprises in financial services, healthcare, biotech and technology rely on us for DSPM, access governance and AI governance (ArgusAI), plus inline controls that stop AI agents from reaching sensitive data.

Why this role matters: our product runs inside customer cloud accounts, scans petabytes, and makes classification and blocking decisions security teams act on. A missed regression can mean a broken scan, a false negative on sensitive data, or an agent reaching data it shouldn’t. Quality here is a security property.

What you’ll do

Test automation: API, UI and end-to-end

• Design and own an automation framework covering our REST/GraphQL APIs, web console and full scan-to-finding workflows.
• Build end-to-end tests that seed known sensitive data, run discovery and classification, and assert on the resulting findings, lineage and access context.
• Test policy and enforcement paths, including inline controls that block AI agents from labeled data, for both correctness and false positives/negatives.
• Keep suites fast and trustworthy: hunt flaky tests, isolate test data, and make failures easy to diagnose.

Cloud and SaaS connectors

• Build test harnesses for connectors across AWS (S3, RDS, DynamoDB), GCP, Azure, Snowflake and SaaS sources like Confluence.
• Validate customer-like deployments: cross-account IAM roles, KMS-encrypted data, scoped (prefix-level) scans, least-privilege permissions and network boundaries.
• Maintain ephemeral, reproducible test environments using infrastructure-as-code.
• Cover failure modes: throttling, expired credentials, partial permissions, schema drift and API changes from third-party providers.

Performance, scale and CI/CD

• Build load and scale tests for scanning and classification across large and wide data estates, and track throughput, latency and cost per scan over time.
• Own quality gates in CI/CD: what runs on every PR, nightly and pre-release, and what blocks a deploy.
• Report quality metrics (coverage, escape rate, flake rate, scan reliability) and drive fixes with engineering leads.
• Partner with engineers on testability in design reviews, and with support on reproducing and regression-testing customer issues.

What you’ll bring

• 5–8 years in SDET, test automation or software engineering roles, with at least 3 years building frameworks rather than only writing test cases.
• Strong coding in TypeScript, Python or Go (UI testing); you write test code to the same standard as production code.
• Hands-on API testing (pytest, REST/GraphQL clients, contract testing) and modern UI automation (Playwright or Cypress).
• Solid AWS experience: IAM roles and cross-account access, S3, KMS, plus Terraform or similar for test environments.
• Experience with CI/CD (GitHub Actions or similar), containers and Kubernetes.
• Performance and load testing with tools like k6, Locust or Gatling, and the judgment to turn results into fixes.
• Comfort testing data-heavy, distributed or asynchronous systems: queues, batch jobs, eventual consistency.
• Clear written communication and the ownership to work remotely across US time zones.

Nice to have

• Experience in data security, DSPM, DLP, CNAPP or other security products.
• GCP, Azure or Snowflake, and SaaS APIs such as Atlassian, Google Workspace or Microsoft 365.
• Testing ML or LLM-based classification: labeled datasets, precision/recall tracking, regression on model changes.
• Graph databases or large-scale metadata systems.
• Experience at an early-stage startup, building QA practices from the ground up.