Data Engineer

hace 2 semanas


Mendoza, Argentina Techunting A tiempo completo
Design and Optimize Scalable Data Infrastructure

At Techunting, we're seeking a highly skilled Data Engineer to join our team. As a key member of our data engineering team, you will be responsible for designing, building, and optimizing scalable ETL pipelines using Apache Spark on Amazon EMR.

You will work closely with data scientists, analysts, and other engineering teams to define, implement, and maintain high-performance data infrastructure. Your expertise in developing and maintaining automated data workflows and processes for efficient data ingestion, transformation, and loading will be crucial in driving innovation in our data infrastructure.

We're looking for someone with a strong understanding of distributed computing concepts, data partitioning, and resource management in large-scale data processing systems. Your proficiency in building and maintaining ETL pipelines for structured and unstructured data, as well as your hands-on experience with AWS services such as S3, Lambda, EMR, Glue, and RDS, will be essential in this role.

As a Data Engineer at Techunting, you will have the opportunity to work on a wide range of projects, from designing and implementing scalable data pipelines to collaborating with stakeholders to understand business requirements and translate them into technical solutions.

Requirements:

  • 5+ years of experience in data engineering, with at least 3+ years working with Apache Spark and Amazon EMR.
  • Strong programming skills in Python and Scala with a focus on performance tuning and optimization for Spark jobs.
  • Deep understanding of distributed computing concepts, data partitioning, and resource management in large-scale data processing systems.
  • Proficiency in building and maintaining ETL pipelines for structured and unstructured data.
  • Hands-on experience with AWS services such as S3, Lambda, EMR, Glue, and RDS.
  • Strong problem-solving skills and ability to debug complex systems.

Preferred Qualifications:

  • Experience with DevOps practices, including CI/CD, infrastructure as code (e.g., Terraform, CloudFormation), and containerization (e.g., Docker).
  • Experience with Kubernetes and container orchestration for Spark jobs.
  • Familiarity with streaming data processing using tools like Kafka, Kinesis, or Flink.
  • Experience with modern data lake architectures, including Delta Lake or Iceberg.
  • AWS Certification (e.g., AWS Certified Big Data – Specialty, AWS Certified Solutions Architect) is a plus.

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