Big Data Engineer

Appsierra Group; Australia; Remote; AUD 88,000 - 234,000; Full time

Job summary

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.

Responsibilities

• 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.

Skills

Python

Big data pipelines

Distributed systems

Data modeling

SQL & NoSQL

Cloud basics

Tools

Hadoop

Spark

Flink

AWS

GCP

Azure

Job description

Big Data Engineer

Contract · Remote

Annual Compensation Equivalent: $62,400–$166,400/year
Hourly Rate: $30–$80/hour

About the Role

This contract role is for a Big Data Engineer experienced in designing and maintaining large-scale data infrastructure, pipelines, and processing systems.

You’ll apply your engineering expertise to build reliable data solutions and contribute high-quality technical input to the training and improvement of next-generation AI systems. The work involves large-scale data processing, Python development, distributed systems, databases, data quality, and governance.

What You’ll Do

• Design, build, and maintain scalable data pipelines and architectures for large-scale data environments.

• Translate business and technical requirements into reliable data engineering solutions.

• Develop data integration, transformation, and processing workflows using Python and appropriate big data technologies.

• Build, manage, and optimize distributed databases and storage systems for performance, reliability, and scalability.

• Monitor data infrastructure, troubleshoot failures, and improve system availability and performance.

• Implement data quality, security, and governance practices across data pipelines and platforms.

• Apply sound data modeling, ETL, and data warehousing principles to support reliable data systems.

• Collaborate with technical and non-technical stakeholders to understand requirements and communicate implementation decisions.

• Document architectures, processes, technical solutions, and operational procedures clearly.

Requirements

• Proven hands-on experience in big data engineering, including building and maintaining large-scale data pipelines.

• Advanced proficiency in Python for data processing, automation, and system integration.

• Strong understanding of relational and NoSQL databases, including database design, optimization, and administration.

• Experience with distributed data processing frameworks such as Hadoop, Spark, or Flink.

• Solid understanding of data modeling, ETL processes, and data warehousing principles.

• Strong troubleshooting and analytical skills for diagnosing performance, reliability, and data-quality issues.

• Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.

• Detail-oriented, proactive, and comfortable working independently in a remote environment.

Preferred Qualifications

• Experience working in fast-paced or startup-like environments.

• Experience collaborating with globally distributed teams.

• Hands-on experience with cloud-based data platforms and services, including AWS, Google Cloud, or Microsoft Azure.

• Familiarity with MLOps, machine learning infrastructure, or data science workflows.

• Experience building data systems that support analytics, machine learning, or other high-volume data applications.

Who Should Apply

This role is suited to data engineers who have practical experience working with large-scale datasets and distributed data systems. You should be comfortable moving between pipeline development, database optimization, data processing, infrastructure reliability, and data governance.

Strong independent problem-solving and communication skills are important, particularly for engineers working remotely and across multidisciplinary teams.

Compensation

• Annual compensation equivalent: $62,400–$166,400

• Hourly rate: $30–$80/hour

• Annual equivalent is calculated at 40 hours per week × 52 weeks per year.

• Actual earnings will depend on hours worked and the duration of the contract.

Work Arrangement

• Contract position

• Fully remote

• Work focused on large-scale data engineering, distributed processing, databases, pipelines, and data infrastructure

• Collaboration with cross-functional technical and business stakeholders