Capgemini's Global Insights & Data business line is a market leader in data, platform, and analytics across various sectors including financial services, consumer products, manufacturing, and life sciences. Join our team as a Data Engineer and take on a crucial role in building and optimizing data infrastructure.
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Your responsibilities will include developing scalable data pipelines, ensuring data integrity, and collaborating with cross-functional teams to enhance data-driven decision-making.
Key Responsibilities:
- Programming Proficiency: Python, Scala, Java, with experience in Apache Spark & Hadoop.
- Database Expertise: Hands-on with SQL & NoSQL (MySQL, PostgreSQL, MongoDB, Cassandra).
- ETL & Data Warehousing: Skilled in data modeling, transformation, and processing.
- Machine Learning Integration: Understands data needs for AI model training.
- Pipeline Development: Experience with Apache Airflow, Luigi, and workflow automation.
- Big Data Technologies: Knowledge of data lakes, real-time processing, and streaming solutions.
- Scalable Data Architecture: Design robust, high-performance data solutions.
- Collaboration & Innovation: Work closely with ML engineers & explore new technologies.
- Version Control & Documentation: Proficient in Git, GitLab, and code management.
Ideal Candidate Profile:
- Expertise in AI & ML: Proficiency in machine learning, NLP, and deep learning.
- Strong Communication Skills: Ability to present complex AI concepts to non-technical stakeholders.
- Problem-Solving Mindset: Innovative thinker with a solutions-driven approach.
- Collaborative Team Player: Experienced in cross-functional teamwork.
- Agile Experience: Preferred background in Agile methodologies.
- Educational Background: Bachelor's in Computer Science, Data Science, IT, or a related field with a focus on data engineering and big data technologies.
Consider joining Capgemini to accelerate your career while contributing to a more sustainable and inclusive future.
