Remote Spark Data Engineer - Build Scalable Pipelines

BairesDev

United States

Remote

USD 110.000 - 165.000

Vollzeit

vor 5 Stunden
Sei unter den ersten Bewerbenden

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Benefits dieser Stelle

100% Remote
Competitive USD compensation
Home office setup
Flexible hours
Paid parental leave
Vacation & holidays
Global multicultural team
Mentorship and growth opportunities

Zusammenfassung

BairesDev, a global tech leader with 15+ years of experience, is hiring a Data Engineer specialized in Spark. This 100% remote role connects you to teams delivering large-scale data pipelines and cloud infrastructure across AWS, Azure, and GCP.

You will design, build, and optimize batch and streaming pipelines with Python and PySpark, use Delta Lake or Iceberg, and orchestrate workflows with Airflow to deliver timely insights.

Qualifikationen

  • 4+ years of experience in Data or Software Engineering.
  • Expertise in building large-scale batch and streaming pipelines with Spark and Python.
  • Hands-on with Spark internals, PySpark, Delta Lake or Iceberg.
  • Proficient in SQL and Airflow for orchestration.
  • Experience with AWS, Azure, or GCP.
  • Advanced English proficiency.

Aufgaben

  • Design and develop robust data pipelines using Python and PySpark.
  • Optimize Spark jobs for performance and cost.
  • Implement scalable storage with Delta Lake or Iceberg.
  • Orchestrate workflows with Airflow.
  • Build data processing infra across AWS/Azure/GCP.
  • Write and optimize complex SQL queries for data transformation.

Kenntnisse

Spark
PySpark
Delta Lake
Apache Iceberg
Python
SQL
Airflow
AWS
Azure
GCP
English

Jobbeschreibung

BairesDev, a global tech leader with 15+ years of experience, is hiring a Data Engineer specialized in Spark. This 100% remote role connects you to teams delivering large-scale data pipelines and cloud infrastructure across AWS, Azure, and GCP.

You will design, build, and optimize batch and streaming pipelines with Python and PySpark, use Delta Lake or Iceberg, and orchestrate workflows with Airflow to deliver timely insights.

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