Maintenance Data Specialist - Powertrain

The Maintenance Data Specialist is responsible for leveraging data analytics to optimize maintenance strategies, improve equipment reliability, and reduce downtime. This role collects, analyzes, and interprets maintenance data from CMMS and production systems to support data-driven decision-making and continuous improvement efforts.

You Will:

· Develop and maintain dashboards and reports to monitor maintenance KPIs (MTTR, MTBF, uptime, compliance, etc.).

· Analyze equipment failure trends and recommend predictive and preventive actions.

· Support migration from time-based to condition- or cycle-based maintenance strategies.

· Work closely with equipment OEM, automation engineers, maintenance supervisor, manufacturing engineers, and technicians to improve asset reliability.

· Identify data gaps and improve CMMS data quality through audits and standardization.

· Collaborate with IT and automation teams to integrate real-time data sources (e.g., SCADA, PLC, sensors).

· Support implementation of predictive maintenance tools using vibration, temperature, and other condition-monitoring inputs.

· Present actionable insights to maintenance and operations leadership for strategy alignment.

· Contribute to annual maintenance budget planning with data-driven forecasts and historical analysis.

You Bring:

· Bachelor’s degree in engineering, Data Science, Industrial Technology, or related field.

· Six Sigma Green Belt + or similar certification.

· 4+ years of experience in maintenance, reliability, or data analytics in a manufacturing environment.

· Strong experience with CMMS platforms (SAP PM, Fiix, Maximo, etc).

· Proficient in data analysis tools (Excel, Power BI, SQL, Python/R is a plus).

· Understanding of maintenance strategies (preventive, predictive, reliability-centered maintenance).

· Strong problem-solving, communication, and collaboration skills.

Preferred Skills:

· Familiarity with IoT and Industry 4.0 technologies.

· Knowledge of manufacturing systems (MES, PLCs).

· Experience with failure mode and root cause analysis tools (FMEA, RCA, 5 Whys).