Ci Data Engineer

hace 5 días

México Chubb A tiempo completo

**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.

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