Data Analyst – Predictive Maintenance

About the role

Leverage data management and analysis to improve plant reliability and efficiency, developing predictive maintenance models, managing data integrations, and ensuring operational data integrity.

Key responsibilities

• Develop and maintain data pipelines that consolidate information from multiple operational and business systems.
• Analyze equipment performance, maintenance, and reliability data to identify trends and improvement opportunities.
• Build predictive maintenance models to forecast equipment failures and support proactive maintenance strategies.
• Create and maintain dashboards, reports, and visualizations that provide actionable insights to maintenance and operations teams.
• Ensure data accuracy, integrity, accessibility, and governance across reliability‑related systems.
• Collaborate with Maintenance, Operations, and Engineering teams to deliver data‑driven solutions.
• Support continuous improvement initiatives through advanced analytics and process optimization.
• Provide training and technical support on data analytics tools and reporting systems.
• Develop and execute the PMO project “Preventive Maintenance Optimization”.
• Analyze performance of preventive maintenance programs and coach execution teams.
• Develop strategic plans for defect elimination and bad‑actor management.
• Contribute to health and safety management, risk identification, and incident reporting.

Required profile

• Minimum of 3 years experience in a Data Engineering role, preferably within manufacturing, mining, or industrial environments.
• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• Strong problem‑solving, critical‑thinking, and analytical abilities.
• Excellent verbal and written communication skills.
• Ability to work collaboratively across Maintenance, Operations, Engineering, Planning, and Supply Chain teams.
• High attention to detail and commitment to data accuracy.
• Continuous improvement mindset with a proactive approach.

Required skills

• Advanced proficiency in Python for data analysis, automation, predictive analytics, and machine learning.
• Strong knowledge of SQL for querying and transforming large operational datasets.
• Proficiency in Microsoft Power BI and other data visualization tools.
• Experience with ERP systems such as Pronto Xi, JD Edwards, SAP or equivalent.
• Familiarity with industrial data historians, SCADA systems, PI System or similar OT platforms.
• Knowledge of cloud‑based analytics and data management platforms.
• Exposure to big‑data technologies such as Apache Spark, Kafka, and Hadoop.
• Understanding of data integration, ETL processes, data quality management, and data governance.
• Strong statistical and analytical skills for trend and anomaly detection.
• Experience developing automated reporting solutions.