The mission of the Advanced Analytics Solution team is to develop and operate end-to-end automation, analytics, and AI assets to enhance company processes and business outcomes. As a Big Data Engineer, the role involves close collaboration with analytics, IT, and architecture teams to develop, industrialize, and maintain data pipelines, analytics/AI models, and automation software for production. Responsibilities also include liaising with the IT department to manage the cloud-based analytics environments utilized by the team. The role encompasses technology scouting and defining solutions and standards for Data & Analytics across the organization, participating in cross-functional working groups at both local and international levels. This position offers a dynamic and innovative work environment with opportunities for professional growth and development.
Key Responsibilities:
Data Pipeline Development: Design, optimize, and re-engineer data pipelines and analytics models to ensure robust and efficient data processing.
Project Delivery: Deliver automation and analytics artifacts in alignment with project timelines and objectives.
Cross-functional Collaboration: Participate in cross-functional working groups to represent the Advanced Analytics team and contribute to the definition of target solutions.
Cloud Infrastructure Management: Assist team members in managing cloud-based analytics infrastructure in coordination with the IT department.
Operational Monitoring: Develop and implement activities required to operate and monitor production projects effectively.
Technology Scouting: Support the identification and evaluation of new technologies, techniques, and software within the Data & Analytics domain.
Key Requirements/Skills/Experience:
Experience: 2+ years of experience in engineering, with a focus on developing and operating analytics/AI assets in large-data contexts.
Education: Master's degree in Computer Science or Computer Engineering. A PhD or post-graduate courses in related fields (e.g., HPC, BDE, DS) is a plus.
Technical Proficiency: Excellent knowledge of Python, PySpark, SQL, and scripting.
Proficiency with orchestration tools such as Airflow.
Strong software engineering skills.
Database Knowledge: Good understanding of relational and non-relational databases, ETL processes, CDC, and data streaming tools and techniques.
Cloud Ecosystems: Experience with Microsoft Azure or AWS ecosystems.
Big Data Architectures: Familiarity with analytics and Big Data architectures (e.g., Kafka, Hadoop, Databricks) and web/cloud technologies (e.g., Docker, Kubernetes).
Language Skills: Fluent in English, both written and spoken.
Soft Skills: Team spirit, self-motivation, and a proactive, result-driven work style.
Strong ability to meet deadlines, with excellent self-organization.
Strong analytical, problem-solving, and communication skills.
Additional Knowledge: Familiarity with statistics and data science topics, MLOps techniques, network communication protocols, and project management will be considered a plus.
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