In this role, you will design and deliver scalable data solutions within Azure environments while serving as a technical reference for the engineering team. Combining hands-on Data Engineering with technical leadership, you will work closely with clients and architects to translate business needs into reliable solutions across the delivery lifecycle, with a focus on data integration, performance, and quality.

Responsibilities
- Design and develop scalable ETL and data integration pipelines across Azure data environments
- Own technical implementation and solution quality, ensuring reliable, scalable, and maintainable data processing
- Collaborate with clients, Solution Architects, and engineering teams to translate business requirements into effective data solutions
- Build and optimize data processing workflows using SQL, Python, Spark, PySpark, Azure Data Factory, and Apache Airflow
- Develop data models and schemas aligned with business requirements, architecture standards, and downstream consumption needs




- Implement data quality and validation controls to maintain data integrity and consistency across platforms
- Optimize SQL queries, ETL processes, and data workflows to improve performance and reliability
- Guide the Data Engineering team on technical decisions, engineering practices, troubleshooting, and successful solution delivery

Requirements
- 5+ years of experience in Data Engineering, including hands-on development of ETL pipelines and data integration solutions
- Advanced proficiency in SQL and Python for data processing, transformation, and optimization
- Strong experience with Spark and PySpark for distributed data processing
- Hands-on experience with Azure data technologies and cloud-based data platforms
- Advanced proficiency with Azure Data Factory and/or Apache Airflow for workflow orchestration and scheduling
- Strong understanding of Data Architecture, Data Modeling, Data Quality, Data Integration, and SQL and ETL performance optimization
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📌 Lead Data Engineer (Colombia)
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