**Job Description: Data Engineer**
**Key Responsibilities**:
- **Data Pipeline Development**:Design, develop, and implement data pipelines and ETL processes using **Python and Databricks and/or IICS**(Informatica).
- **Collaboration**:Work closely with data scientists and analysts to understand data requirements and deliver solutions that meet business needs.
- **Optimization**:Optimize existing data workflows for performance, scalability, and reliability.
- **Monitoring and Troubleshooting**:Monitor and troubleshoot data pipeline issues, ensuring timely resolution to maintain data integrity and availability.
- **Documentation**:Document data processes, workflows, and technical specifications to ensure clarity and maintainability.
- **Continuous Learning**:Stay updated with the latest industry trends and technologies related to data engineering, cloud services, and data integration.
**Qualifications**:
- **Technical Expertise**:
- Proven experience in Python programming and data manipulation.
- Mid-level knowledge of Databricks and its ecosystem (e.g., Spark, Delta Lake).
- Experience with IICS (Informatica)



or similar cloud-based data integration tools.
- Familiarity with SQL and database management systems (e.g., PostgreSQL, MySQL).
- Understanding of data warehousing concepts and best practices.
- Experience with cloud platforms (e.g., AWS, Azure, GCP).
- **Problem-Solving and Optimization**:
- Mid-level problem-solving skills with a focus on addressing performance issues in data workflows.
- Experience implementing optimization techniques and evaluating performance improvements using measurable metrics.
- **Best Practices**:
- Understanding of data engineering best practices, including data quality, reliability, and maintainability.
- Knowledge of robust monitoring practices and data governance principles.
- **Soft Skills**:
- Strong communication and collaboration skills.
- Familiarity with Agile development methodologies.
**Preferred Skills**:
- Hands-on experience with data storage solutions such as data lakes and data warehouses.
- Insights into common pitfalls in the data lifecycle and strategies to avoid them.

📌 Ci Data Engineer (México)
🏢 Chubb
📍 México