Specialist Data Engineer & AI

About the Company

As a global leader in semiconductor solutions in power systems and IoT, Infineon enables game-changing solutions for green and efficient energy, clean and safe mobility, as well as smart and secure IoT. Together, we drive innovation and customer success, while caring for our people and empowering them to reach ambitious goals. We are on a journey to create the best Infineon for everyone.

We strive to create the best Infineon for everyone by embracing diversity and inclusion. We welcome applicants for who they are and offer a workplace built on trust, openness, respect and equal opportunity. Our hiring decisions are based on skills and experience. Even if you don't meet every requirement, we encourage you to apply. Please let your recruiter know if you need any accommodations during the interview process.

Role Overview

We are seeking a highly motivated Data Engineer & AI Specialist to drive digital transformation and data-driven decision-making within the Quality organisation. In this role, you will design, develop, and manage scalable data solutions, AI applications, and advanced analytics capabilities that support quality improvement, manufacturing excellence, and business performance.

You will collaborate closely with Quality, Manufacturing, Engineering, IT, and Business stakeholders to build trusted data pipelines, implement AI and machine learning solutions, develop actionable dashboards, and generate insights that improve product quality, operational efficiency, and customer satisfaction. The ideal candidate combines strong technical expertise in data engineering, cloud technologies, and AI with a business-oriented mindset and the ability to translate complex data into measurable business value.

Key Responsibilities

Data Engineering & Architecture

• Design, develop, and maintain scalable data pipelines, models, and products to support Quality and Manufacturing use cases.
• Collect, integrate, process, and transform structured and unstructured data from manufacturing systems, testing platforms, quality databases, customer feedback systems, and enterprise applications.
• Ensure data reliability, availability, scalability, and performance across quality-related analytics solutions.
• Support data governance, master data management, data lineage, and data quality initiatives across the organization.

Artificial Intelligence & Advanced Analytics

• Develop, deploy, and monitor machine learning, predictive analytics, and AI models to improve quality performance and operational efficiency.
• Apply advanced statistical analysis and AI techniques to identify trends, anomalies, root causes, and predictive quality risks.
• Participate in AI experimentation, proofs-of-concept, and innovation initiatives aligned with business priorities.
• Collect, integrate, process, and transform structured and unstructured data from manufacturing systems, testing platforms, quality databases, customer feedback systems, and enterprise applications.
• Ensure the responsible and compliant use of AI solutions in accordance with corporate governance and data privacy requirements.

Business Intelligence & Reporting

• Develop and maintain interactive dashboards, KPI frameworks, and self-service analytics solutions using Power BI and other visualization tools.
• Translate business requirements into meaningful metrics and data-driven insights.
• Create executive-level reports and visualizations to support operational and strategic decision-making.
• Define and monitor quality performance indicators and continuous improvement metrics.
• Ensure the responsible and compliant use of AI solutions in accordance with corporate governance and data privacy requirements.

Collaboration & Business Partnership

• Partner with Quality, Manufacturing, Engineering, and Digitalization teams to identify opportunities for data-driven improvements.
• Lead workshops and requirements-gathering sessions with stakeholders to define analytics and AI use cases.
• Promote knowledge sharing and contribute actively to internal data and AI communities.
• Drive adoption of data-driven decision-making throughout the organization.

Innovation & Continuous Improvement

• Proactively identify opportunities to leverage data, automation, and AI to generate business value.
• Stay current with emerging technologies, industry trends, and best practices in Data Engineering, AI, and Quality Management.
• Contribute to digital transformation roadmaps and strategic data initiatives.

Qualifications and Skills

Education

• Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related field.

Professional Experience

• One to two years of experience strongly preferred in Data Engineering, Data Science, AI, Business Intelligence, or Advanced Analytics.
• Experience in semiconductor, manufacturing, quality management, or industrial environments is preferred.
• Experience delivering end-to-end data and analytics solutions from data ingestion through business consumption.

Technical Skills

• Strong programming skills in Python and SQL.
• Experience with data engineering frameworks, ETL/ELT processes, and modern data architectures.
• Knowledge of cloud-based analytics platforms such as Azure, AWS, or Google Cloud.
• Experience with Power BI and enterprise reporting solutions.
• Experience developing, deploying, and monitoring machine learning models.
• Understanding of MLOps, DataOps, CI/CD, and software development best practices.
• Familiarity with Generative AI, Large Language Models (LLMs), and AI integration patterns is an advantage.
• Strong understanding of database technologies, data warehousing, and data modelling concepts.

Business & Leadership Competencies

• Strong analytical and problem-solving capabilities.
• Ability to translate business challenges into scalable data and AI solutions.
• Excellent communication and stakeholder management skills.
• Experience consulting with business functions and influencing decision-making through data.
• Strong collaboration skills and ability to work effectively in cross-functional teams.
• Continuous learning mindset with a passion for innovation and digital transformation.
• Ability to work in a fast-paced environment and manage multiple priorities.

Preferred Competencies

• Knowledge of Quality Management systems and methodologies, including ISO 9001, Six Sigma, Statistical Process Control, and FMEA.
• Experience with manufacturing execution systems (MES), test systems, or semiconductor data environments.
• Certification in Data Engineering, Cloud Platforms, AI, or Analytics technologies.

Benefits

• Career development & learning: About 17,000 free LinkedIn Learning courses in 10 languages, Mentoring & Coaching programs, Global Onboarding process as well as Virtual & f2f trainings.
• Time & flexibility: Flexible working hours & part-time work, Mobile work (specifications based on job profile and local requirements), Remote work from abroad / Workation (EU), Sabbatical (EU).
• Health & wellbeing: Health activities & programs, Health & Well-being LinkedIn Learning Journey, Health & Well-being Community.
• Your working environment: Employee events, Open & global community (e.g., cross-site exchange through virtual meetings), Open office spaces.
• Rewards & benefits: Fair and transparent remuneration, Success participation, Performance-related recognition, customized benefit packages.