Data Engineer iv)*

• End-to-End Data Product Ownership: Design, build, and operate scalable, reliable data pipelines that serve as the foundational backbone for enterprise analytics and machine learning solutions.
• Cross-Functional Collaboration: Lead centralized data initiatives by partnering with Data Scientists, Analysts, and domain experts to deliver high-quality, business-aligned data products.
• Operational Excellence & Reliability: Ensure data stability and integrity through rigorous monitoring, automated validation, and continuous improvement of data flows and ML operationalization.
• Technical Leadership & Best Practices: Champion DataOps, clean code, and robust testing standards while acting as a role model for engineering excellence within the team.
• Strategic Innovation & Modernization: Proactively identify opportunities to simplify pipelines, automate workflows, and evaluate emerging cloud technologies to drive platform evolution.
• Scalable Architecture & Future-Readiness: Enable new data sources and analytical use cases by designing extensible architectures that support future forecasting and planning needs., 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. Be a part of making life easier, safer and greener. Are you in?

Do you have experience in Spark?, You show strong communication and collaboration skills to effectively engage with both technical and non-technical stakeholders. Additionally, you demonstrate a passion for emerging technologies and an entrepreneurial mindset to proactively explore, pilot, and scale innovative data solutions.

• Education & Core Experience: A degree in IT, Computer Science, or Engineering, coupled with at least 3 years of experience designing, implementing, and supporting data engineering or MLOps solutions.
• Data Architecture & Governance: Solid grasp of data architectures (Warehouses, Lakes), pipeline design, data quality assurance, and security protocols.
• Technical Proficiency: Strong command of Python, SQL, and distributed storage systems (e.g., HDFS, Oracle, MySQL), along with expertise in API-based integration and query languages like Spark.
• Modern Toolsets & Orchestration: Hands-on experience with big data technologies (Hadoop, Kafka, Hive), containerization (Docker, Kubernetes), and workflow orchestration tools (Airflow, Mage AI).
• DevOps & Enterprise Platforms: Practical knowledge of DataOps/DevOps practices (Git, CI/CD, Jenkins) and familiarity with enterprise data platforms (e.g., Denodo, Dataiku) is highly valued.
• Analytical Problem Solving: Demonstrated ability to apply strong analytical skills to solve complex challenges within a cross-functional, enterprise environment.