Lead / Principal Test Development Engineer (Technology) - Contract

quesscorp singapore pte. ltd.

Singapore

On-site

SGD 180,000 - 260,000

Full time

4 days ago

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Job summary

quesscorp singapore pte. ltd. seeks a Lead/Principal Test Development Engineer to shape strategy, architecture, and scalable test solutions for data-intensive, AI-driven systems.

You will lead QA frameworks, automate testing, validate AI/ML models, and mentor teams across Engineering, Data Science, and DevOps to deliver reliable production systems and measurable business impact. Ideal candidates will bring deep expertise in software quality engineering, test automation, performance engineering,

Qualifications

Bachelor's or higher in Automation Engineering Technology, Computer Science, Software Engineering, Information Systems, Data Engineering
10+ years of experience in software testing, quality engineering, or test development
Working experience on e‑commerce platforms is mandatory
Strong experience working onsite in China and Singapore is mandatory
Proven experience testing large‑scale distributed systems and data platforms
Strong background in AI/ML application testing and recommendation systems validation
Extensive experience building automation frameworks from the ground up
Experience leading major quality transformation initiatives
Demonstrated success in high-growth and technology-driven environments
AI recommendation systems and intelligent customer targeting platforms
Financial technology, credit systems, tax systems, or regulated platforms
Encryption, security, and high-throughput transaction processing systems
Data quality assurance for enterprise-scale analytics environments
Blockchain-related testing, cryptographic platforms, or asset verification systems
Performance optimization projects delivering significant gains in system throughput and scalability
Development of internal testing platforms, quality engineering tools, or enterprise automation frameworks

Responsibilities

Define and execute the organization's quality engineering and test technology roadmap.
Serve as the technical authority for test automation, performance engineering, AI system validation, and distributed systems testing.
Establish best practices, standards, and governance across software quality engineering initiatives.
Mentor test engineers and software development teams on modern testing methodologies and quality-driven development practices.
Lead cross-functional quality initiatives involving Engineering, Data Science, Product Management, DevOps, and Infrastructure teams.
Design and execute test strategies for AI-powered recommendation and decision-making systems.
Validate machine learning models for accuracy, reliability, scalability, robustness, and fairness.
Build testing frameworks for recommendation engines, prediction models, ranking algorithms, and personalization systems.
Develop methodologies for data quality validation, model drift detection, algorithm benchmarking, and regression testing.
Establish monitoring and validation mechanisms for production AI services.
Design comprehensive test plans and test cases for large-scale distributed applications built on Hadoop, Spark, Flink, Kafka, Debezium, Cloud-native data platforms.
Validate end-to-end data ingestion, transformation, processing, storage, and reporting pipelines.
Develop automated validation frameworks for data integrity, consistency, reconciliation, and lineage verification.
Ensure reliability and quality of real-time and batch processing systems.
Lead performance testing initiatives for enterprise-scale systems, APIs, data platforms, and microservices architectures.
Design and execute load, stress, endurance, scalability, and capacity testing programs.
Drive throughput optimization and latency reduction initiatives.
Develop performance test frameworks using JMeter, K6, Grafana, InfluxDB, Prometheus.
Analyze bottlenecks and work closely with engineering teams to optimize system architecture and infrastructure performance.
Architect and develop enterprise-level automated testing frameworks.
Build reusable automation platforms supporting UI Testing, API Testing, Integration Testing, Data Testing, Regression Testing, End-to-End Testing.
Develop automation solutions using Python, Selenium, Behave, PyTest, CI/CD pipelines.
Promote BDD, TDD, and continuous testing practices.
Drive automation adoption to significantly reduce manual testing effort and improve release velocity.
Design and implement comprehensive data validation frameworks.
Develop automated solutions to verify Data completeness, data accuracy, data consistency, data reconciliation, regulatory reporting data
Support quality assurance for large-scale financial, taxation, compliance, and analytical data platforms.
Build synthetic data and data-mocking platforms to accelerate testing activities.
Lead root cause investigations for critical production incidents.
Establish preventive quality controls to eliminate recurring defects.
Drive defect prevention and continuous quality improvement programs.
Ensure production readiness through risk assessment, resilience validation, and operational testing.
Lead development of internal quality engineering platforms and productivity tools.
Build centralized solutions for Test execution management, Performance testing, Test reporting, Data generation, Test environment orchestration
Improve engineering efficiency through automation and self-service capabilities.

Skills

Test strategy

Quality engineering

Test automation

Software testing

Mentoring

Education

Bachelor's or higher in CS/Engineering

Tools

Python

Selenium

Kubernetes

JMeter

Prometheus

Job description

Position Summary

We are seeking a highly skilled Lead / Principal Test Development Engineer (Technology) to lead the strategy, architecture, and implementation of scalable test solutions for data-intensive, AI-driven, distributed, and mission-critical systems.

The ideal candidate will possess deep expertise in software quality engineering, test automation, performance engineering, big data technologies, AI/ML system validation, and distributed platforms. This role requires a hands‑on technical leader who can establish quality frameworks, drive automation maturity, optimize system performance, and ensure the reliability of large-scale production systems.

The successful candidate will have a proven record of improving system quality, automating testing at scale, validating intelligent recommendation algorithms, and delivering measurable business impact through engineering excellence.

Key Responsibilities

Technical Leadership

Define and execute the organization's quality engineering and test technology roadmap.
Serve as the technical authority for test automation, performance engineering, AI system validation, and distributed systems testing.
Establish best practices, standards, and governance across software quality engineering initiatives.
Mentor test engineers and software development teams on modern testing methodologies and quality-driven development practices.
Lead cross‑functional quality initiatives involving Engineering, Data Science, Product Management, DevOps, and Infrastructure teams.

AI, Machine Learning & Recommendation System Testing

Design and execute test strategies for AI‑powered recommendation and decision‑making systems.
Validate machine learning models for accuracy, reliability, scalability, robustness, and fairness.
Build testing frameworks for recommendation engines, prediction models, ranking algorithms, and personalization systems.
Develop methodologies for data quality validation, model drift detection, algorithm benchmarking, and regression testing.
Establish monitoring and validation mechanisms for production AI services.

Big Data & Distributed Systems Testing

Design comprehensive test plans and test cases for large-scale distributed applications built on:
Hadoop
Spark
Flink
Kafka
Debezium
Cloud‑native data platforms
Validate end-to‑end data ingestion, transformation, processing, storage, and reporting pipelines.
Develop automated validation frameworks for data integrity, consistency, reconciliation, and lineage verification.
Ensure reliability and quality of real‑time and batch processing systems.

Performance & Scalability Engineering

Lead performance testing initiatives for enterprise‑scale systems, APIs, data platforms, and microservices architectures.
Design and execute load, stress, endurance, scalability, and capacity testing programs.
Drive throughput optimization and latency reduction initiatives.
Develop performance test frameworks using:
JMeter
K6
Grafana
InfluxDB
Prometheus
Analyze bottlenecks and work closely with engineering teams to optimize system architecture and infrastructure performance.

Test Automation Architecture

Architect and develop enterprise‑level automated testing frameworks.
Build reusable automation platforms supporting:
UI Testing
API Testing
Integration Testing
Data Testing
Regression Testing
End‑to‑End Testing
Develop automation solutions using:
Python
Selenium
Behave
PyTest
CI/CD pipelines
Promote BDD, TDD, and continuous testing practices.
Drive automation adoption to significantly reduce manual testing effort and improve release velocity.

Data Quality Engineering

Design and implement comprehensive data validation frameworks.
Develop automated solutions to verify:
Data completeness
Data accuracy
Data consistency
Data reconciliation
Regulatory reporting data
Support quality assurance for large‑scale financial, taxation, compliance, and analytical data platforms.
Build synthetic data and data‑mocking platforms to accelerate testing activities.

Reliability & Production Quality

Lead root cause investigations for critical production incidents.
Establish preventive quality controls to eliminate recurring defects.
Drive defect prevention and continuous quality improvement programs.
Ensure production readiness through risk assessment, resilience validation, and operational testing.

Platform & Tooling Development

Lead development of internal quality engineering platforms and productivity tools.
Build centralized solutions for:
Test execution management
Performance testing
Test reporting
Data generation
Test environment orchestration
Improve engineering efficiency through automation and self-service capabilities.

Required Qualifications

Bachelor's or higher in Automation Engineering Technology, Computer Science, Software Engineering, Information Systems, Data Engineering

10+ years of experience in software testing, quality engineering, or test development.
Working experience on e‑commerce platforms is mandatory
Strong experience working onsite in China and Singapore is mandatory.

Proven experience testing large‑scale distributed systems and data platforms.
Strong background in AI/ML application testing and recommendation systems validation.
Extensive experience building automation frameworks from the ground up.
Experience leading major quality transformation initiatives.
Demonstrated success in high‑growth and technology‑driven environments.
AI recommendation systems and intelligent customer targeting platforms.
Financial technology, credit systems, tax systems, or regulated platforms.
Encryption, security, and high‑throughput transaction processing systems.
Data quality assurance for enterprise‑scale analytics environments.
Blockchain‑related testing, cryptographic platforms, or asset verification systems.
Performance optimization projects delivering significant gains in system throughput and scalability.
Development of internal testing platforms, quality engineering tools, or enterprise automation frameworks.