We are looking for an experienced Principal Software Quality Engineer to define and drive the quality strategy for a distributed database platform. This is a senior individual-contributor role requiring deep expertise in distributed database testing, systems engineering, debugging, and test automation.

The ideal candidate will have hands-on experience working with distributed database or storage systems, preferably NoSQL platforms. They should be comfortable reading server-side code, tracing complex defects to their root cause, designing fault-injection scenarios, and collaborating with QE, client, and server engineering teams to ensure product reliability.

The role will also lead the integration of AI agents into testing frameworks, enabling automated test generation, failure triage, and evaluation of agent reliability.

Quality Strategy and Test Architecture

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

Drive quality across server components, client SDKs, operator and 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 coverage for critical database operations and ensure quality requirements are incorporated early in the development lifecycle.

Distributed Systems and Fault-Injection Testing

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

Design and execute fault-injection scenarios using process crashes, GDB-driven state manipulation, packet drops, latency injection, corrupted wire data, oversized inputs, and version skew between master and replica nodes.

Identify failure scenarios that conventional positive testing cannot adequately cover.

Reproduce defects at the source-code level, identify suspect functions, and provide developers with minimal reproducible test cases and detailed root-cause analysis.

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

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

AI-Agent Integration and Testing

Lead the integration of AI agents into existing testing frameworks.





Design agent workflows that can plan and execute tests, interact with tools, inspect logs, traces, metrics, and database state, and generate actionable defect reports.

Develop evaluation frameworks using golden datasets, graders, and regression suites to measure the reliability and accuracy of AI-generated outputs.

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

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

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

Drive debugging and defect-resolution efforts across QE, client, and server engineering teams.

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

10–18 years of experience in Software Quality Engineering, SDET, or Systems Engineering, with a strong focus on quality systems and demonstrated Staff or Principal-level technical impact.

- Deep 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.
- Strong experience testing replication, partitioning, sharding, consistency, transactions, secondary indexes, backup and restore, and cross-datacenter replication.
- Ability to read and debug server-side code written in C, C++, Go, Rust, or Java and trace defects to specific functions and root causes.
- Strong software engineering and automation skills in Python, with proficiency in at least one systems programming language.
- Practical experience with fault injection, GDB, chaos-style testing, process termination, network partitions, latency and packet-loss injection, disk faults, corrupted inputs, version skew, and boundary-value testing.
- Experience across multiple testing layers, including unit, integration, API, contract, end-to-end, performance, scalability, upgrade/downgrade, and soak testing.
- Proven ability to lead debugging and root-cause investigations across QE, client,



and server teams, providing actionable technical analysis and following defects through to resolution.
- Robust CI/CD experience with GitHub Actions, Jenkins, or equivalent tools, along with experience managing shared test infrastructure.
- Practical experience using AI agents for testing, including agent workflows, tool integration, prompt and evaluation design, and integration with real-world testing frameworks.
- Experience testing client SDKs across multiple programming languages, including Python, Java, Go, C, Node.js, and .NET.

Experience testing backup and restore, checkpointing, snapshotting, and WAL/journal correctness, including performance at terabyte scale.

Expertise in query-engine and secondary-index testing, including query planning, index selection, pagination, boundary values, and encoding/collation correctness.

Kubernetes and database operator testing experience using tools such as KUTTL, Chainsaw, or envtest.

Cloud infrastructure experience across AWS, GCP, or Azure, including TLS/mTLS, IAM, secrets management, and air-gapped installations.

Security-focused testing experience, including fuzzing, CVE reproduction, and identifying memory-safety issues such as out-of-bounds reads and use-after-free defects in native code.

Critical database workflows, including replication, rebalancing, consistency, transactions, secondary indexes, backup and restore, and cross-datacenter replication, have comprehensive and reliable test coverage.

Critical regressions are identified before release through high-signal, low-flakiness automated tests.

Defect reports consistently include minimal reproducible cases, root-cause analysis, and suspected source-code locations.

Development teams experience shorter defect-resolution cycles across the areas owned by the role.

AI agents become an integral part of the testing framework, supporting test generation and failure triage with measurable reliability through evaluation suites.

Test-farm flakiness decreases, upgrade/downgrade coverage improves, and new features have test plans before implementation is complete.

Engineering teams across QE, client, and server functions adopt the testing frameworks, practices, and quality standards introduced by the role.

This is a senior technical individual-contributor position, not a manual QA or people-management role. The candidate is expected to write framework code, debug server processes, investigate low-level system behavior, evaluate AI-agent performance, and build reliable testing infrastructure that other engineers can operate and extend.

Distributed Database

Software Testing

Database Testing

📌 Principal Software Quality Engineer (Permanent) (India)
🏢 Socnet Technologies Private
📍 India