Predictive Maintenance & Analytics Transformation Specialist

Airbus; Getafe; Presencial; EUR 65.000 - 80.000; Jornada completa

Descripción de la vacante

A leading aerospace company located in Getafe seeks a Predictive Maintenance Specialist to drive the development and implementation of predictive maintenance models. The ideal candidate will have a Master’s degree and over 5 years of experience in the aviation sector, specializing in aircraft systems and data analytics. Responsibilities include working with engineering teams, ensuring data integrity, and developing dashboards for real-time insights. Join us and contribute to enhancing aircraft reliability and optimizing maintenance operations.

Formación

• 5+ years of experience in predictive maintenance, reliability engineering, or data analytics within the aviation sector.
• Deep understanding of aircraft systems (avionics, engines, etc.).
• Strong background in signal processing and fault detection.

Responsabilidades

• Drive design and deployment of predictive maintenance models.
• Collaborate with engineering teams to identify degradation patterns.
• Develop dashboards and real-time maintenance insights.

Conocimientos

Predictive maintenance

Data analytics

Artificial Intelligence

Communication skills

Team spirit

Educación

Master’s degree in Engineering or related field

Herramientas

TensorFlow

Python

SQL

Spark

AWS

Descripción del empleo

Job Description

We are seeking a highly skilled Predictive Maintenance Specialist with strong expertise in Aircraft Systems, Data Analytics, and Artificial Intelligence (AI). The ideal candidate will drive the development and implementation of predictive maintenance models and data‑driven solutions to enhance aircraft reliability, reduce unscheduled downtime, and optimize maintenance operations.

The candidate will join the Health Management and Predictive Analytic team.

The analytics approach can have several applications, such as the anticipation of the failure of an aircraft component, the diagnosis of a failure that has already occurred, the identification of the triggering process, detection of an anomaly in the system, prediction of the lifetime of a component, root‑cause analysis of a failure, and optimisation of troubleshooting tasks.

Responsibilities

• Drive the design, development and deployment of predictive maintenance models to anticipate aircraft component failures and optimise maintenance schedules.
• Establish and maintain data‑integrity requirements to ensure the data set needed to feed the predictive maintenance models.
• Participate in defining the certification basis for CBM applications ensuring compliance with aviation authorities and alignment with safety and reliability objectives.
• Integrate and analyse flight data, health monitoring system (HMS) outputs, sensor data, and maintenance logs.
• Collaborate with avionics, propulsion, and airframe engineering teams to interpret system behaviours and identify degradation patterns.
• Develop dashboards, KPIs, and data pipelines for real‑time maintenance insights.
• Implement data preprocessing, feature engineering, and anomaly‑detection methods.
• Lead the validation and continuous improvement of predictive algorithms in operational environments.
• Support digital twin and reliability‑centered maintenance (RCM) initiatives.
• Ensure compliance with aviation standards in predictive maintenance applications.
• Communicate technical findings to non‑technical stakeholders through clear visualisations and reports.
• Analyse aircraft problems from a Systems Engineering point of view: understanding system operation and potential contributions to system failures.
• Document, prepare and present the model development process and results graphically and visually.

Required Qualifications/Competencies

• Master’s degree in Engineering, Data Science, Artificial Intelligence, or a related field.
• 5+ years of experience in predictive maintenance, reliability engineering, or data analytics within the aviation sector.
• Deep understanding of aircraft systems (avionics, engines, hydraulics, pneumatics, environmental control, etc.), especially valuable in A400M, MRTT, M&L and new developments.
• Proficiency with machine‑learning frameworks (e.g., TensorFlow, PyTorch, scikit‑learn) and data‑analytics tools (Python, SQL, MATLAB).
• Experience with big‑data architectures (e.g., Spark, Hadoop, or cloud‑based analytics environments such as AWS, Azure, or GCP).
• Strong background in signal processing, time‑series analysis, and fault detection.
• Good communication skills, assertiveness and willingness to travel.
• Commitment, proactiveness and team spirit is a must.

Preferred Qualifications

• Experience with digital twin technology or fleet‑level predictive maintenance programmes.
• Knowledge of aircraft health monitoring systems (AHMS) and prognostics & health management frameworks.
• Knowledge of CBM (Condition‑Based Maintenance).
• Familiarity with ARINC, AFDX and ATA standards for aircraft data exchange.
• Experience integrating AI models with maintenance information systems (MIS) or enterprise asset‑management (EAM) platforms.
• Data analytics: data cleaning, manipulation and processing; descriptive, diagnostic, predictive and prescriptive analytics focused on aircraft systems, using techniques for pattern recognition, data mining and design of experiments.
• Knowledge of AI techniques or development of complex algorithms focused on time‑series, NLP and image processing.
• Programming knowledge: advanced level of Python and its main libraries for analytics and data science such as Pandas, Numpy, Matplotlib, Seaborn, Bokeh, Scipy, Scikit‑learn, TensorFlow, Keras, NLTK, SQLAlchemy.
• Knowledge of data‑visualisation tools such as Spotfire, Tableau, PowerBI.
• Knowledge of developing queries for the extraction and manipulation of data: SQL.
• Basic knowledge of relational and non‑relational databases.
• Knowledge of GIT, version control and collaborative development.
• General knowledge of the complete V&V development cycle.

This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.

Company

Airbus Defence and Space SAU

Employment Type

Permanent

Experience Level

Professional

Job Family

Computing&Comm and Info & Data Processing

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