Zealogics Inc

Security Engineer

• Lead the assessment, scoping, and architectural design of strategic RACF and USS security project solutions
• Define target-state architectures and technical designs
• Support deployment of security solutions
• Assess technical feasibility and trade-offs of proposed solutions
• Collaborate with various teams to gather requirements
• Create and maintain security monitoring, automation, and reporting solutions
• Monitor, analyze, and report on key performance indicators

Mainframe Security Architect, RACF Architect Consultant

Data Engineer

• Develop, design, and maintain Power BI dashboards, reports, and data visualizations based on business requirements.
• Build efficient data models (star schema, semantic models) using Power BI and Microsoft Fabric components.
• Work with Microsoft Fabric features such as Lakehouse, Data Engineering pipelines, Dataflows Gen2, and Warehouse where applicable.
• Optimize datasets, visuals, DAX queries, and report performance.
• Collaborate with business stakeholders and technical teams to gather requirements and translate them into scalable reporting solutions.
• Ensure data accuracy, governance, and compliance with organizational standards.
• Troubleshoot issues related to datasets, refresh failures, permissions, and report performance.
• Support the modernization of analytics platforms and the adoption of Microsoft Fabric within the KPMG ecosystem.

Senior Data Engineer – PowerBI, Fabric

Cloud Engineer

• Collaborate with teams to design and implement Azure solutions
• Develop and maintain CI/CD pipelines
• Automate infrastructure provisioning and configuration management
• Monitor system performance and ensure reliability

Azure Engineer

Data Analyst

• Lead the maintenance, governance, and continuous improvement of AI Operations dashboards, scorecards, KPIs, and reporting assets.
• Ensure operational, consumption, billing, and performance reporting remains accurate, reliable, and actionable.
• Track adoption, usage, consumption, cost, billing, capacity, and operational performance trends across AI platforms and services.
• Deliver recurring executive, operational, financial, and consumption reporting packages.
• Analyze operational and financial data to identify trends, risks, opportunities, and areas requiring attention.
• Investigate reporting discrepancies, consumption anomalies, and unexpected usage patterns to determine root causes and recommend corrective actions.
• Perform deep-dive analysis of AI operational, financial, adoption, and consumption data.
• Correlate data from multiple sources to provide meaningful insights and recommendations.
• Develop executive-ready summaries, trend analysis, and operational performance narratives.
• Oversee consumption monitoring, utilization reporting, billing visibility, and cost transparency processes.
• Support forecasting, budgeting, chargeback, and show back reporting activities.
• Identify opportunities to improve operational efficiency and optimize resource utilization.
• Partner with Finance to support AI consumption reporting and cost management activities.
• Maintain reporting standards, data quality controls, metric definitions, support documentation, and reporting governance processes.
• Ensure consistency and integrity of operational, financial, and consumption reporting.
• Support operational audits, compliance reporting, and governance reviews.
• Partner with Operations, Support, Engineering, and Finance teams to resolve reporting questions, data issues, and operational reporting needs.

AI Operations Reporting & Analytics Analyst

Engineering Manager

• Lead and develop the engineering team while strengthening technical capabilities across multiple customer organizations.
• Identify, recruit, and deploy qualified engineering resources to meet customers' manpower and project requirements.
• Prepare Statements of Work (SOW), quotations, and commercial proposals based on customer requirements.
• Provide timely support for urgent customer issues, including emergency manpower allocation and resolution of technical or personnel-related challenges.
• Evaluate and promote opportunities for Taiwan-based manufacturing and outsourcing partnerships.
• Leverage strong hands-on experience in semiconductor equipment to understand customer challenges, provide practical solutions, and support technical decision-making.
• Support customer performance evaluations of assigned engineers and drive continuous improvement based on customer feedback.
• Resolve conflicts and communication issues between customers and their internal engineering teams.
• Regularly report customer business status, manpower deployment, project progress, and business development updates to management.
• Support business development activities and undertake additional assignments as required.
• Hands-on experience in system/module assembly, integration, troubleshooting, and on-site technical support.
• Solid understanding of assembly processes, system and hardware integration, testing, debugging, and manufacturing workflows within semiconductor equipment companies.

Engineering Director – Associate Director

• Design and implement enterprise data architectures including Data Lakes, Data Warehouses, Data Fabric, and MDM solutions.
• Build scalable data pipelines and data integration frameworks.
• Define data governance, quality, lineage, and security standards.
• Enable AI/ML initiatives through robust data engineering and architecture practices.
• Collaborate with business and technology teams to develop data strategies and roadmaps.
• Optimize data platforms for performance, scalability, and reliability.

Senior Data Architect, Data Engineer – AI & Data Platforms

Backend Engineer

• Participate in algorithm development for inspection and metrology
• Collaborate with architects, designers, and engineers to develop robust computer vision modules
• Work with Product Management to prioritize feature development and breakdown tasks
• Drive infrastructure and architectural optimization solutions
• Perform code reviews and ensure proper design and delivery
• Assessment and improve algorithm performance
• Software development based on product/project requirements.
• Inspection flow and inspection module performance evaluation and optimization.
• Software troubleshooting and bug fixing.
• Cross-nation and cross-team communication to understand the tasks and ensure projects are on time and teamwork smoothly.

Senior C++ Developer, Image Processing

DevOps Engineer

• Cooperating with software teams and maintaining the build scripts
• Improving site reliability
• Enforcing good software development practices using infrastructure as code (IAC) methodology
• Site reliability/uptime management
• CI/CD maintenance and automation of CI/CD processes
• Security monitoring and patching
• Downtime and patching communication with internal/external stakeholders
• System upgrades
• New tool implementation and onboarding
• Other DevOps related issues included but not limited to above

Senior DevOps Engineer – SRE

Data Scientist

• Ensure master data is AI‑ready, supporting machine learning, analytics, and Generative AI use cases.
• Collaborate with Data Science and AI teams to: Define training‑data requirements, Prevent bias and duplication in AI inputs, Improve feature quality through high‑quality master data, Implement AI‑assisted data matching, classification, and enrichment where applicable.
• Support explainability, lineage, and traceability requirements for AI and ML models.
• Enable natural‑language and AI‑driven access to master data for analytics and decision support platforms.
• Work closely with MDM platforms and tools. Define and oversee integration patterns between MDM, ERP, CRM, data platforms, and AI pipelines.
• Collaborate with data engineering teams on cloud‑based data platforms, APIs, and automation workflows.
• Ensure master data availability and consistency across operational, reporting, and AI systems.
• Lead and mentor Master Data Stewards and Analysts.
• Act as a key stakeholder in enterprise Data & AI initiatives.
• Drive organizational adoption of data governance and AI‑driven data quality practices. Communicate MDM and AI value clearly to technical and non‑technical stakeholders.