As a Data Analyst - RAM at INNIO (Reliability, Availability and Maintainability), you will support data-driven reliability engineering by analyzing Jenbacher gas engines fleet operational data to quantify RAM performance. The role focuses on transforming raw operational data into actionable reliability insights that inform product design, validation, and lifecycle optimization.
- Young & supportive team - in a fast-growing international business environment
- Learn & develop yourself - help you craft your career path with (INNIO learning, free language courses according to business needs
- Hybrid working - 50% Office, 50 % Home Office with flexible working hours
- Cafeteria package with various options - SZÉP Card, Cash option, Nursery & kindergarten support
- Medicover Spring White Package- from the first day of employment
- Easily accessible location - in the heart of the most vibrant campus area in Budapest (location: 13rd District, easy to approach by Metro Line 3)
- Take a break - Recharge your batteries with free coffee and tea selection in the kitchen area
- Live an active lifestyle – activate your AYCM pass
Your Responsibilities:
- Analyze fleet operational datasets to assess reliability, availability, and maintainability performance
- Perform statistical analysis of asset operation and failures
- Extract, clean, and transform large datasets using Python and SQL (Postgres, ClickHouse) from operational databases
- Use advanced Python for repeatable and scalable RAM analysis
- Support reliability analyses with quantitative failure and downtime data
- Create clear data visualizations and reports to communicate insights to engineering and management
- Collaborate with reliability engineers, digital engineering, and product development teams
- Contribute to continuous improvement of data quality, analytics methods, and RAM standard
Your Profile:
- Bachelor’s degree in Engineering, Data Science, Statistics, Mathematics, or similar
- 2+ years of experience in data analytics, reliability engineering, or asset performance analytics
- Strong proficiency in Python for data analysis (pandas, numpy, plotly, dash)
- SQL skills for querying and joining complex datasets
- Experience working with time-series and event-based data
- Ability to structure and document analytical methods for reuse
- Good understanding of statistics
- Strong analytical and problem-solving mindset
- Ability to translate complex data into clear engineering insights
