Data Engineer Lead - Build Automated Data Pipelines & BI

Blend360; Bogotá; Presencial; COP 110.000.000 - 170.000.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

AWS Certification access

Databricks/ Snowflake training

Udemy Business access

English lessons

Travel opportunities

Mentoring programs

Company equipment

Flexible working options

Descripción de la vacante

Blend360 is seeking a Data Engineer Lead to design, develop, and support automated data pipelines and BI solutions for reporting and analytics. You will modernize manual processes into scalable workflows and support production model scoring and data prep.

The role emphasizes collaboration with business and technical teams, ensuring reliable operation of analytical models and data quality across projects.

Formación

• Proven track record delivering production reporting and analytics solutions.
• Strong SQL Server development and complex query optimization.
• Proficient in Python for automation and data processing; Databricks experience.
• Experience designing end-to-end data pipelines and BI solutions.

Responsabilidades

• Design, develop, and maintain automated data pipelines.
• Automate manual reporting processes and repetitive data workflows.
• Optimize queries, data models, and processing performance.
• Support production data preparation and model scoring for analytics.
• Monitor data quality and troubleshoot pipeline issues.
• Collaborate with stakeholders to deliver scalable data solutions.
• Promote data engineering best practices and continuous improvements.

Conocimientos

SQL Server

Python

Databricks

Tableau

Data modeling

SQL optimization

BI development

Automation

Herramientas

Databricks

Tableau

Descripción del empleo

Blend360 is seeking a Data Engineer Lead to design, develop, and support automated data pipelines and BI solutions for reporting and analytics. You will modernize manual processes into scalable workflows and support production model scoring and data prep.

The role emphasizes collaboration with business and technical teams, ensuring reliable operation of analytical models and data quality across projects.