Reliability Engineer, Data Analytics
Konecranes B.V. · Hyvinkää, Finland
- Location
- Hyvinkää, Finland
- Work location
- Hybrid
- Posted
- Today
About the job
Company Description
At Konecranes, we believe that great customer experience is built on the people behind the Konecranes name. Everything we do, we do with passion and drive. We believe diversity drives business success and is the foundation for our growth. We welcome different backgrounds and skills that enrich our community and we promote a place where we can ALL be ourselves. This is what makes Konecranes a unique place to work.
Job Description
Are you passionate about data analytics, engineering, and turning data into actionable insights?
Konecranes Reliability Center is looking for a Reliability Engineer, Data Analytics to strengthen our team. In this role, you will help us improve product reliability, support product development, and generate valuable business insights by analysing data from products, services, testing, and field operations.
We are looking for a curious and motivated individual who enjoys solving complex problems and is eager to learn. While previous work experience is beneficial, your willingness to develop, learn quickly, and grow as an expert is what matters most to us.
Your key responsibilities
As a Reliability Engineer, Data Analytics, you will
- Analyze reliability, service, usage, and testing data from various sources
- Develop insights that support product development, service business, and quality improvement
- Participate in reliability investigations, root cause analyses, and claim assessments
- Support predictive maintenance and reliability modelling initiatives
- Create reports, visualizations, and presentations that help stakeholders make data-driven decisions
- Collaborate with product development, service, quality, and other business functions
- Contribute to the continuous development of Konecranes' reliability analytics capabilities
Qualifications
What you should bring
- Master's degree in Data Analytics, Computer Science, or other engineering field
- A few years experience in product related big data analytics or product knowledge from machine industry
- Strong analytical and problem-solving skills
- Interest in data analysis, reliability engineering, and industrial products
- Ability to learn new tools, technologies, and business domains quickly
- Good communication skills in English
The following are considered an advantage
- Experience with statistical analysis, machine learning, data based forecasting, and data visualization
- Experience with Databricks, Python, SQL, Power BI, and Git
- Experience working with IoT, and time series data
Additional Information
Why join us?
At Konecranes Reliability Center, you will have the opportunity to work with unique datasets, modern analytics methods, and highly skilled experts in reliability engineering. You will contribute directly to improving the reliability, safety, and sustainability of Konecranes products while building your own expertise in an international environment.
Interested?
If you are passionate about learning, data, and engineering, we would love to hear from you. Please send us your application in English by 8 November 2026 at the latest.
Join us and help shape the future of reliability through data-driven insights.
Have questions? Feel free to contact Marion Soon, Global Talent Acquisition Specialist, marion.soon[ at ]konecranes.com
Konecranes moves what matters. We are a global leader in material handling solutions, serving a broad range of customers across multiple industries. We consistently set the industry benchmark, from everyday improvements to the breakthroughs at moments that matter most, because we know we can always find a safer, more productive and sustainable way. That's why, with 16 000+ professionals in over 50 countries, we are trusted every day to lift, handle and move what the world needs.
Konecranes is committed to ensuring that all employees and job applicants are treated fairly in an environment which is free from any form of discrimination.
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