Senior Data Engineer | Bees Personalization

Responsibilities

• Leads efforts within the organization to drive the development and maintenance of data services and solutions to support products, downstream services, or infrastructure tools and platforms used across BEES.
• Design efficient data models and understand concepts such as normalization, denormalization, and dimensional modeling.
• Implement improvements in architecture and processes to improve the performance, monitoring, and evolution of data products.
• Develop and maintain data ingestion, processing, control/security, and data provisioning processes for different consumers (services, front-end, among others).
• Contribute to obtaining knowledge in the business context and creating new data products to meet their strategic and operational needs.

Requirements and qualifications

• Degree in Computer Science, Computer Engineering, Information Systems, Systems Development Analysis or similar;
• Advanced English;
• Assess scalability, reliability, security, and compliance implications of data pipeline designs.
• Understand cloud computing platforms and services offered by providers like AWS, Azure, and Google Cloud.
• Evaluate programming solutions in Python, PySpark, Scala, and SQL for data processing and analysis.
• Evaluate data quality processes and controls to ensure accuracy and completeness.
• Apply debugging techniques to identify and resolve cross-module issues.
• Implement monitoring, alerting, and failure handling mechanisms in architecture designs.
• Assess effectiveness of CI/CD principles and practices in automated pipelines.
• Design, develop, and maintain APIs for data exchange between applications.
• Apply Agile practices to plan, execute, and deliver data engineering tasks and projects.

Soft Skills

• Logical reasoning and analytical skills;
• Meeting deadlines and quality of work;
• Effective and transparent dialog with other areas and co-workers;
• Ability to communicate and interpersonal relationships to present cases and discuss them with other areas involved;
• Being independent in activities;
• Work as part of a team, promoting a good relationship with the team;

Differentials

• Evaluate data pipeline solutions for latency, throughput and accuracy.
• Analyze data requirements and business needs to inform data modeling decisions.
• Understand the principles and assumptions of different machine learning capabilities.
• Integrate data sets with visualization tools to create insightful reports and dashboards.