• Application Deadline: Wed, 15 Oct 2025 00:00:00 GMT
• Position: Data Analyst



• Job Type Full Time



• Qualification BA/BSc/HND



• Experience 4 - 6 years



• Location Ogun




• Job Field Data, Business Analysis and AI 
  
  

Job Role

• The Data Analyst will play a critical role in supporting Terra Aqua Environmental Consultancy Limited’s Metal Recycling Plant operations by collecting, analyzing, and interpreting production, maintenance, financial, and supply chain data.


• The role is responsible for transforming raw operational data into actionable insights that drive efficiency, sustainability, compliance, and profitability.


• The analyst will work closely with Operations, Maintenance, ESG, Finance, and Senior Management to design reporting systems, build dashboards, and provide decision-support analyses.


• This is an experienced-level position requiring a strong background in industrial/manufacturing data analytics, proven ability to design and maintain databases/dashboards, and the ability to work with large, complex datasets.

Key Responsibilities

Data Management & Reporting:

• Design, implement, and maintain databases for production, equipment, maintenance, and other operational data.


• Ensure accuracy, completeness, and timeliness of daily data capture (production logs, equipment performance, maintenance logs, energy use).


• Develop and maintain real-time dashboards and reports in tools such as Power BI, Tableau, or Airtable.


• Establish and enforce data governance standards across departments.

Production & Process Analytics:

• Analyze furnace-level data (inputs, hot ingots, recovery %, capacity utilization) to identify efficiency trends, bottlenecks, and anomalies.


• Monitor scrap feedstock quality, yield, and recovery efficiency to recommend process improvements.


• Provide variance analysis between planned vs actual production and recommend corrective actions.

Maintenance & Asset Analytics:

• Track downtime, repair costs, spare parts usage, and preventive maintenance schedules.


• Support predictive maintenance by analyzing breakdown history and usage trends.


• Provide cost-per-equipment and lifecycle insights to support replacement vs repair decisions.

Financial & Business Analytics:

• Collaborate with Finance to monitor unit cost of production, repair costs, and margins.


• Build cost models for scrap procurement, energy usage, and logistics.


• Provide management with profitability and efficiency dashboards.

Stakeholder Communication & Decision Support:

• Present insights in clear, business-friendly reports and presentations for management and board meetings.


• Support regulators and investors with accurate, auditable data.


• Train operations staff on data capture tools and dashboard usage.

Key Performance Indicators (KPIs) for the Role

• Accuracy and completeness of daily production and maintenance data.


• Number and quality of dashboards/reports automated.


• Reduction in downtime through predictive analytics insights.


• Improvement in production yield/recovery through data-driven recommendations.


• Management satisfaction with data quality and insights.

Requirements

Educational Qualification

• Bachelor’s Degree in Data Analytics, Statistics, Computer Science, Engineering, Industrial/Production Management, or related field.


• Master’s Degree in Data Science, Business Analytics, or related field is an advantage.

Experience:

• Minimum 4–6 years of proven experience as a Data Analyst in manufacturing, heavy industry, recycling, or related environment.


• Demonstrated track record in designing dashboards, building data pipelines, and conducting in- depth operational analytics.


• Experience with production process data preferred.

Technical Skills:

• Strong proficiency in data visualization & reporting tools: Power BI, Tableau, or equivalent.


• Advanced Excel skills (pivot tables, power query, VBA).


• Proficiency with databases and SQL for managing large datasets.


• Experience with Python or R for advanced analytics and automation will be an advantage.


• Familiarity with ERP systems and integration of operational data.


• Knowledge of predictive analytics for maintenance (machine learning preferred but not required).

Industry Knowledge:

• Understanding of industrial production processes, furnaces, recycling, or heavy equipment operations will be an advantage.


• Familiarity with supply chain and logistics analytics in a manufacturing context.

Soft Skills:

• Strong analytical and problem-solving skills.


• Ability to translate complex data into actionable insights for non-technical stakeholders.


• Excellent written and verbal communication skills.


• High attention to detail and data accuracy.


• Ability to work cross-functionally with operations, maintenance, finance, and ESG teams.