Accountabilities:

Define and own the overall quality strategy for the distributed database platform, including test architecture, coverage models, release criteria, and risk-based prioritization.

Drive quality across database server components, client SDKs, operators, backup tooling, and management interfaces.

Design comprehensive testing strategies for replication, rebalancing, partitioning, sharding, consistency models, transactions, isolation, secondary indexes, query planning, compaction, backup and restore, snapshots, and cross-datacenter replication.

Establish reliable test coverage for critical database operations and ensure quality and testability requirements are incorporated early in the software development lifecycle.

Validate cluster correctness under production-like failure conditions, including node crashes, network partitions, clock skew, disk failures, rolling upgrades, mixed-version deployments, and long-running soak tests.

Design and execute fault-injection scenarios involving process crashes, GDB-driven state manipulation, packet drops, latency injection, corrupted protocol data, oversized inputs, and version incompatibilities.

Identify complex failure scenarios that conventional functional testing cannot adequately detect.

Investigate low-level system defects involving memory boundaries, encoding and decoding inconsistencies, compression, CRC errors, and distributed consistency violations.

Reproduce defects at the source-code level, identify potential root causes and affected functions, and provide engineering teams with minimal reproducible test cases and actionable technical analysis.

Build and maintain scalable automated testing frameworks using Python, pytest, custom harnesses, API testing, and end-to-end automation.

Ensure test suites operate reliably across shared testing infrastructure and CI/CD environments, including GitHub Actions and Jenkins.

Improve test infrastructure reliability by reducing flaky tests, investigating noisy failures, and strengthening regression coverage.

Develop testing strategies across unit, integration, API, contract, end-to-end, performance, scalability, upgrade/downgrade, and long-running regression testing.

Lead the integration of AI agents into existing testing frameworks, designing workflows capable of planning and executing tests, interacting with tools, analyzing logs and metrics, and generating actionable defect reports.

Develop AI-agent evaluation frameworks using golden datasets, graders, and regression suites to measure reliability, accuracy, and consistency.

Integrate AI agents with pytest, custom test harnesses, and MCP-style tool servers.

Enable AI-assisted test generation, edge-case discovery, flaky-test maintenance, and failure triage while maintaining appropriate human oversight.

Participate in feature reviews, product requirements discussions, design reviews, and client specifications to establish testability requirements before implementation.

Develop test plans for new features and collaborate with engineering teams to validate technical designs.

Convert production incidents and pre-release defects into permanent regression tests, monitoring checks, and preventive measures.

Lead technical debugging and defect-resolution efforts across quality engineering, client engineering, and server engineering teams.

Mentor senior Quality Engineers and SDETs in debugging techniques, test architecture, and framework development.

Establish technical quality standards and engineering practices that can be adopted across teams without direct people-management responsibilities.

Drive measurable improvements in regression prevention, test infrastructure stability, defect resolution time, and AI-assisted testing effectiveness.

Requirements

10–18 years of experience in Software Quality Engineering, Software Development Engineer in Test (SDET), or Systems Engineering, with significant technical impact at Staff or Principal level.

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.

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?

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