Lead / Principal Test Development Engineer (Technology) - Contract

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.

Technical Expertise

Programming & Automation

• Python (Expert)
• Selenium
• Behave
• PyTest
• REST API Testing
• UI Automation
• Test Framework Design

Big Data Technologies

• Hadoop
• Spark
• Flink
• Kafka
• Debezium
• Data Warehousing Platforms
• ETL/ELT Frameworks

Performance Engineering

• JMeter
• K6
• Grafana
• InfluxDB
• Load Testing
• Throughput Analysis
• Capacity Planning

Cloud & DevOps

• Docker
• Kubernetes
• Jenkins
• GitLab CI/CD
• Monitoring & Observability Solutions

Data & AI Quality

• Data Pipeline Validation
• Recommendation System Testing
• ML Model Validation
• Data Reconciliation
• Statistical Analysis
• Algorithm Verification

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