Databricks Data Engineer: Lakehouse ETL Architect (Remote)

BairesDev; Distrito Capital; Remote; VES 89,286,000 - 142,857,000; Full time

Benefits offered by this job

100% remote work

USD compensation

Home-office setup

Flexible hours

Parental leave & holidays

Global, multicultural team

Job summary

Join BairesDev as a Data Engineer (Databricks) and shape scalable data pipelines across Databricks environments. You will bridge raw data sources with analytics-ready datasets, leveraging lakehouse architectures to ensure data consistency and reliability.

You will design end-to-end ETL pipelines using PySpark and SQL, collaborate with data scientists, and maintain data quality through monitoring and schema enforcement.

Qualifications

• 4+ years of experience in Data or Software Engineering.
• Databricks and PySpark pipeline development experience.
• Strong SQL and data warehousing concepts.
• Hands-on experience building and maintaining data lakehouse architectures.
• Deep understanding of ETL/ELT processes and large-scale data transformations.
• Fluent English.

Responsibilities

• Design and implement end-to-end ETL pipelines using PySpark and SQL within the Databricks platform.
• Build and optimize data transformations following the Medallion Architecture to create high-quality Bronze, Silver, and Gold layers.
• Develop declarative pipelines and manage complex orchestration workflows to ensure reliable production deployments.
• Collaborate with data scientists and analysts to provide clean, structured data for advanced analytics and machine learning workloads.
• Ensure data integrity and performance through proactive monitoring, schema enforcement, and automated data quality checks.
• Maintain and scale lakehouse environments to handle diverse structured and unstructured datasets efficiently.

Skills

Databricks

PySpark

SQL

Data warehousing

Lakehouse

ETL/ELT

English

Job description

Join BairesDev as a Data Engineer (Databricks) and shape scalable data pipelines across Databricks environments. You will bridge raw data sources with analytics-ready datasets, leveraging lakehouse architectures to ensure data consistency and reliability.

You will design end-to-end ETL pipelines using PySpark and SQL, collaborate with data scientists, and maintain data quality through monitoring and schema enforcement.