Accountabilities:

Quality Strategy and Test Architecture: Define and own the overall quality strategy for the distributed database platform, including test architecture, coverage models, release criteria, and risk-based testing priorities. Drive quality across server components, client SDKs, database operators, backup tools, and management interfaces.
Distributed Database Testing: Design and execute comprehensive testing strategies covering replication, rebalancing, partitioning, sharding, consistency models, transactions, isolation, secondary indexes, query planning, compaction, backup and restore, snapshots, and cross-datacenter replication.
Fault-Injection and Reliability Testing: Validate cluster behavior under production-like failure conditions, including node crashes, network partitions, clock skew, disk failures, latency, rolling upgrades, mixed-version deployments, and long-running stress tests.
Advanced Debugging and Root-Cause Analysis: Investigate complex system-level defects involving memory access, serialization, encoding and decoding, compression, checksums, and consistency violations. Reproduce issues at source-code level, identify potential root causes, and provide engineering teams with minimal reproducible test cases and actionable technical reports.
Test Automation and Framework Development: Build, maintain, and improve scalable automated testing frameworks using Python, pytest, custom test harnesses, API testing, and end-to-end automation. Ensure reliable execution across shared testing infrastructure and CI/CD environments such as GitHub Actions and Jenkins.
Regression and Infrastructure Reliability: Strengthen regression coverage, reduce flaky tests, investigate inconsistent test results, and develop testing strategies across unit, integration, API, contract, end-to-end, performance, scalability, upgrade/downgrade, and soak testing.
AI-Agent Integration: Lead the integration of AI agents into existing testing frameworks, enabling automated test planning, execution, log inspection, metrics analysis, defect reporting, and failure triage. Integrate agents with pytest, custom testing harnesses, and MCP-style tool servers.
AI Evaluation and Quality Assurance: Establish evaluation frameworks using golden datasets, graders, and regression suites to measure the reliability, accuracy, and consistency of AI-generated outputs while maintaining appropriate human oversight.
Cross-Functional Collaboration: Participate in product requirement discussions, feature reviews, technical design reviews, and client specification assessments to establish testability requirements early in the development lifecycle.
Technical Leadership and Mentorship: Lead complex debugging and defect-resolution initiatives across quality, client, and server engineering teams. Mentor senior QEs and SDETs, promote effective debugging practices, and establish reusable engineering standards without requiring direct people-management responsibilities.
Continuous Quality Improvement: Convert production incidents and pre-release defects into permanent regression tests, monitoring checks, and preventive measures to improve overall platform reliability.

Requirements

Professional Experience: 10–18 years of experience in Software Quality Engineering, SDET, or Systems Engineering, with demonstrated Staff or Principal-level technical impact and a strong focus on building quality systems.
Distributed Database Expertise: Extensive hands-on experience testing distributed databases or storage systems, preferably NoSQL platforms such as Cassandra, ScyllaDB, MongoDB, Couchbase, Redis, DynamoDB, or HBase. Experience with distributed relational databases such as CockroachDB, TiDB, YugabyteDB, or Spanner-style systems is also relevant.
Database Testing Knowledge: Strong experience validating replication, partitioning, sharding, consistency, transactions, secondary indexes, backup and restore, and cross-datacenter replication.
Distributed Systems Fundamentals: Deep understanding of replication protocols, consensus mechanisms, quorum reads and writes, strong and eventual consistency, split-brain scenarios, clock synchronization issues, and failure-recovery semantics.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

#LI-CL1