Data Analytics Engineer

Job Overview:
This role involves developing automated reports and dashboards, identifying trends and performance gaps, and applying statistical analysis and machine learning techniques to improve productivity, quality, yield, and operational efficiency.

Primary Responsibilities:
1. Automation and Reporting
• Automate recurring reports and data analysis processes using Python, SQL, and other suitable tools.
• Develop scripts to reduce manual data processing and improve reporting accuracy.
• Optimize existing reporting workflows and data management processes.
• Maintain technical documentation for data pipelines, dashboards, and analytical models.

2. Statistical Analysis and Machine Learning
• Apply statistical methods to identify relationships, trends, anomalies, and potential root causes.
• Develop and evaluate machine learning models for forecasting, classification, anomaly detection, and predictive analytics, where appropriate.
• Support predictive maintenance, yield improvement, quality monitoring, and process optimization initiatives.
• Evaluate model performance and communicate results in a clear and practical manner.

3. Cross-Functional Collaboration
• Work closely with engineering, manufacturing, quality, IT, and management teams to understand business requirements.
• Translate operational challenges into data-driven analytical solutions.
• Recommend process improvements based on analytical findings.
• Ensure data confidentiality, integrity, and compliance with company policies.

4. Data Analytics and Visualization
• Develop interactive dashboards, reports, and visualizations using Power BI, Tableau, or other business intelligence tools.
• Analyze large datasets to identify trends, patterns, correlations, and performance gaps.
• Monitor key performance indicators (KPIs), including productivity, yield, defect rates, downtime, and operational efficiency.
• Present analytical findings and recommendations to management and relevant stakeholders.

5. Data Collection and Processing
• Collect, consolidate, clean, and validate data from multiple sources, including databases, production systems, and Excel files.
• Develop and maintain data pipelines to ensure data accuracy, consistency, and availability.
• Perform data transformation and preprocessing for reporting, statistical analysis, and predictive modelling.
• Identify data quality issues and implement appropriate corrective measures.

Qualification & Experience:
Education Requirement:
Bachelor's Degree in Data Analytics, Data Science, Computer Science, Information Technology, Statistics, Engineering, or a related discipline.
Candidates with relevant diplomas and strong practical experience may also be considered, depending on company requirements.

Additional Desirable Qualifications:
Experience in manufacturing, semiconductor, electronics, or other industrial environments is an added advantage.