Lead Data Engineer: Databricks & Medallion Architecture

OZ; Argentina; Presencial; ARS 2.000.000 - 3.200.000; Jornada completa

Descripción de la vacante

OZ is seeking a Lead Data Engineer to own solution design, mentor engineers, and steer data platform development. You will design and optimize Databricks ETL pipelines, implement medallion data models, and enable analytics-ready views across business domains.

Collaborating with data stewards, IT, and business stakeholders, you will drive architectural decisions, ensure data quality, and push for best practices while documenting knowledge for sustainable delivery.

Formación

• 5+ years delivering in Databricks environments
• Experience with medallion architecture in data platforms
• Strong communication and collaboration skills
• Experience with BI tooling (Power BI/Tableau/Qlik)

Responsabilidades

• Serve as technical lead and mentor engineers
• Design, develop, and maintain ETL pipelines in Databricks
• Implement medallion architecture data models (raw, curated, trusted)
• Develop dimensional models for analytics dashboards and KPIs
• Collaborate with data stewards, IT, and stakeholders to translate requirements
• Contribute to architectural decisions and best practices
• Address data quality issues and bottlenecks early
• Support knowledge transfer and documentation

Conocimientos

Databricks

Medallion Architecture

Dimensional Modeling

ETL Orchestration

Business Acumen

Self-Starter

Clear Communication

Team Collaboration

Educación

Bachelor's degree in Computer Science or related field

Herramientas

Databricks

Azure Data Lake

Data Factory

Power BI

Tableau

Qlik

Descripción del empleo

OZ is seeking a Lead Data Engineer to own solution design, mentor engineers, and steer data platform development. You will design and optimize Databricks ETL pipelines, implement medallion data models, and enable analytics-ready views across business domains.

Collaborating with data stewards, IT, and business stakeholders, you will drive architectural decisions, ensure data quality, and push for best practices while documenting knowledge for sustainable delivery.