Data Engineer: Build Scalable Pipelines for AI Insights

SunnyData; Argentina; Presencial; ARS 900.000 - 1.500.000; Jornada completa

Descripción de la vacante

SunnyData in Argentina seeks a Data Engineer to design, build and deploy data solutions that power AI, ML and BI initiatives. You will work directly with customers and internal teams to capture, transform, and utilize data at scale.

You will leverage cloud platforms (AWS/Azure/GCP), Spark/Hadoop/Kafka, and SQL data warehousing to deliver robust data pipelines and data products that support decision making. Join a collaborative and innovative team driving data-driven outcomes.

Formación

• Bachelor’s degree in Computer Science or Engineering or equivalent.
• Strong written and verbal English communication.
• Experience with cloud platforms AWS, Azure or GCP.
• Familiarity with Spark, Hadoop, Kafka in data pipelines.
• Experience with SQL data warehousing and analytics.
• Databricks data engineering or ML certification is a plus.

Responsabilidades

• Support customers in data engineering needs and projects.
• Guide customers to make technical decisions to meet goals.
• Collaborate across teams to deliver data solutions and educate users.
• Design, build and operate data pipelines and analytics components.
• Test data movement and transformations for quality and reliability.

Conocimientos

English proficiency

AWS

Azure

GCP

Spark

Hadoop

Kafka

Pandas

scikit-learn

HPO

SQL

Educación

Bachelor’s Degree in Computer Science or Engineering

Herramientas

Databricks certification

Descripción del empleo

SunnyData in Argentina seeks a Data Engineer to design, build and deploy data solutions that power AI, ML and BI initiatives. You will work directly with customers and internal teams to capture, transform, and utilize data at scale.

You will leverage cloud platforms (AWS/Azure/GCP), Spark/Hadoop/Kafka, and SQL data warehousing to deliver robust data pipelines and data products that support decision making. Join a collaborative and innovative team driving data-driven outcomes.