Descripción del puesto

About the Role
As a Senior Data Engineer, you’ll be responsible for building and maintaining robust, scalable data pipelines that transform raw information into reliable, actionable insights. You’ll collaborate closely with data scientists, analysts, and business stakeholders, contributing to high-impact AI and data science initiatives within the aviation industry.

Key Responsibilities

• Data Engineering & Automation: Design, implement, and optimize scalable data pipelines for ingestion, transformation, and integration of data from multiple sources. Support the deployment of AI models into production through workflow automation.

• Data Modelling & Transformation: Develop efficient data models and schemas aligned with analytics needs. Clean, enrich, and transform datasets for usability and performance in reporting and machine learning use cases.

• Data Quality, Governance & Security: Define and enforce data quality standards. Proactively monitor and resolve data issues, ensuring compliance with data security and privacy regulations.

• Collaboration & Mentoring: Work closely with cross-functional teams to gather requirements and deliver data solutions. Contribute to documentation and support the growth of junior engineers through knowledge sharing and best practices.

Requisitos

Requirements

• Bachelor’s or Master’s degree in Data Engineering, Computer Science, or related fields.

• 4+ years of experience in data engineering or similar roles, with strong skills in data warehousing, ETL, and integration.

• Proficiency in Python and SQL; experience with Python libraries for data processing.

• Hands-on experience with Snowflake (or ClickHouse) and working within AWS environments.

• Solid understanding of data modelling, governance, and quality best practices.

• Strong communication and collaboration skills; comfortable in cross-functional teams.

Nice to Have

• Experience with orchestration tools (e.g., Airflow, Prefect).

• Familiarity with AMOS Database is a plus.

• Knowledge of containerization (Docker, Kubernetes) and CI/CD tools (e.g., Jenkins, GitHub Actions).

• Exposure to Infrastructure as Code (Terraform, CloudFormation).

• Basic understanding of ML infrastructure and data science workflows.