Remote Big Data Engineer — Scalable Data Pipelines

Recruitment Room; Australia; À distance; AUD 88 000 - 234 000; Plein temps

Résumé du poste

Appsierra Group in Australia is seeking a Big Data Engineer for a contract, remote role. You will design, build and maintain large-scale data pipelines and architectures.

You will apply Python development and distributed systems expertise to processing, storage, and governance, supporting analytics and AI model training.

Collaboration with stakeholders and clear documentation of architectures and procedures are essential to ensure reliability and scalability.

Qualifications

• Proven hands-on big data engineering experience.
• Advanced Python for data processing.
• Strong knowledge of relational and NoSQL databases.
• Experience with Hadoop, Spark, or Flink.
• Solid data modeling, ETL, and data warehousing understanding.
• Strong troubleshooting and analytical skills.
• Ability to communicate complex concepts clearly.
• Detail-oriented and comfortable in a remote environment.

Responsabilités

• Design, build, and maintain scalable data pipelines and architectures for large-scale data environments.
• Translate requirements into reliable data engineering solutions.
• Develop ETL workflows using Python and big data technologies.
• Manage distributed databases for performance and scalability.
• Monitor data infrastructure and improve system availability.
• Apply data quality, security, and governance practices.
• Collaborate with stakeholders to communicate decisions.
• Document architectures and procedures clearly.

Connaissances

Python

Big data pipelines

Distributed systems

Data modeling

SQL & NoSQL

Cloud basics

Outils

Hadoop

Spark

Flink

AWS

GCP

Azure

Description du poste

Appsierra Group in Australia is seeking a Big Data Engineer for a contract, remote role. You will design, build and maintain large-scale data pipelines and architectures.

You will apply Python development and distributed systems expertise to processing, storage, and governance, supporting analytics and AI model training.

Collaboration with stakeholders and clear documentation of architectures and procedures are essential to ensure reliability and scalability.