Data Engineer - Remote, End-to-End Cloud Data Pipelines

BairesDev; United States; Remote; MXN 2,198,000 - 2,748,000; Full time

Benefits offered by this job

Remote work

USD or local currency pay

Home setup for remote work

Flexible hours

Paid leave

Job summary

BairesDev is seeking a Data Engineer to work with a modern, cloud-native data stack to solve complex, large-scale data challenges that influence global decision-making. Building and optimizing data pipelines, you'll power how thousands of users access and leverage high-quality data across the organization.

This is your opportunity to take end-to-end ownership of data solutions, from design to production, while tackling challenging problems like distributed processing, schema evolution, and

Qualifications

• 5+ years of total experience in Data Engineering.
• 3+ years with AWS Glue or Databricks.
• 3+ years with Spark.
• 2+ years with Delta Lake.
• 5+ years with Python for ETL and automation.
• Advanced proficiency in English.

Responsibilities

• Design and implement scalable, secure data pipelines using AWS Glue or Databricks.
• Develop and optimize distributed Spark processes for large volumes of data.
• Manage and maintain Delta Lake tables, ensuring integrity, versioning, and schema evolution.
• Write and maintain clean, modular Python code for ETL and automation.
• Collaborate with cross-functional teams to translate business needs into reliable data solutions.

Skills

Data pipeline design

Distributed processing

Python for ETL

Data quality & schema evolution

Observability & monitoring

Tools

AWS Glue

Databricks

Spark

Delta Lake

SQL

Job description

BairesDev is seeking a Data Engineer to work with a modern, cloud-native data stack to solve complex, large-scale data challenges that influence global decision-making. Building and optimizing data pipelines, you'll power how thousands of users access and leverage high-quality data across the organization.

This is your opportunity to take end-to-end ownership of data solutions, from design to production, while tackling challenging problems like distributed processing, schema evolution, and