Remote Databricks Data Engineer - Lakehouse Pipelines

BairesDev; Chile; Remote; CLP 88,063,000 - 146,771,000; Full time

Benefits offered by this job

100% remote work

USD or local currency compensation

Hardware provided

Flexible hours

Parental leaves & vacations

Multicultural/global team

Mentorship & growth

Job summary

BairesDev is hiring a Data Engineer (Databricks) to design and maintain scalable data pipelines in Databricks environments. You will transform raw data into analytics-ready datasets, applying lakehouse architectures for data consistency and reliability.

Responsibilities include building end-to-end ETL pipelines with PySpark/SQL, implementing Bronze-Silver-Gold layers, and collaborating with data scientists to support ML workloads.

Qualifications

• 4+ years of experience in Data Engineering or Software Engineering.
• Proven expertise in developing and optimizing data pipelines using Databricks and PySpark.
• Strong proficiency in SQL and experience with data warehousing concepts.
• Hands-on experience building and maintaining data lakehouse architectures.
• Deep understanding of ETL/ELT processes and large-scale data transformations.
• Advanced proficiency in 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 Bronze, Silver, and Gold layers.
• Develop declarative pipelines and manage complex orchestration workflows for reliable production deployments.
• Collaborate with data scientists and analysts to provide clean, structured data for analytics and ML workloads.
• Ensure data integrity and performance through monitoring, schema enforcement, and data quality checks.
• Maintain and scale lakehouse environments to handle diverse datasets.

Skills

Databricks

PySpark

SQL

Data warehousing

ETL/ELT

English

Tools

Databricks

PySpark

Job description

BairesDev is hiring a Data Engineer (Databricks) to design and maintain scalable data pipelines in Databricks environments. You will transform raw data into analytics-ready datasets, applying lakehouse architectures for data consistency and reliability.

Responsibilities include building end-to-end ETL pipelines with PySpark/SQL, implementing Bronze-Silver-Gold layers, and collaborating with data scientists to support ML workloads.