Graduate Data Engineer

Chrysos Corporation; Australia; Remote; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Learning budget

Performance bonuses

Wellbeing support

Remote-first organisation

Job summary

Halo Labs is a data solutions firm that is building scalable cloud-native data platforms. We are hiring a data engineer to design, develop, and optimize data pipelines across Azure and AWS, using PySpark, Python, and Databricks to deliver production-ready solutions.

You will collaborate with software engineers to embed data services into the SaaS product, drive data modelling and governance, and implement observability and CI/CD for data workloads in a remote-first environment.

Qualifications

• Strong experience with Python, SQL and PySpark for large-scale distributed data processing.
• Hands-on with Azure Databricks, Azure Data Factory and related Azure services.
• Experience building production-grade data pipelines in a SaaS/product-led environment.
• Knowledge of data modelling, governance, and security for healthcare or regulated data environments.

Responsibilities

• Design, develop and maintain reusable in-house PySpark frameworks across the SaaS platform.
• Architect and implement scalable ETL/ELT pipelines on Azure and AWS.
• Build distributed data processing solutions using Python and PySpark on Databricks.
• Design secure data integration patterns (REST APIs, SFTP, event-driven ingestion, webhooks).
• Implement CI/CD pipelines for automated deployment and testing of data workloads.

Skills

Python

SQL

PySpark

CI/CD

Observability

Data governance

Education

Degree in Computer Science, Engineering, Data Science, or related technical field

Tools

Azure Databricks

Azure Data Factory

Azure SQL

Azure Storage

Azure DevOps

AWS S3

AWS Lambda

AWS RDS

AWS Glue

Job description

Halo Labs is a future focused, end-to-end data solutions firm – transforming tomorrow, today. Our intelligent and secure technology systems and data driven solutions drive meaningful outcomes and unlock lasting value.

Why work at Halo Labs?

• Leading Innovation: We don’t just solve problems; we illuminate a never-ending stream of innovation.
• Exceptional Perks: Enjoy outstanding perks like a dedicated learning budget, performance bonuses, and comprehensive wellbeing support.
• Remote-First Organisation: Experience the perks of remote work while having the flexibility to travel to client locations throughout Australia.
• Inclusive and Engaging: Celebrate diversity in a welcoming space that thrives on new ideas and open conversations, all in a respectful environment.
• Inspiring Origins: With a compelling founder story, we are a customer-focused, culture-first organisation.

About the role

• Design, develop, and maintain reusable in-house PySpark frameworks to enforce standardised data engineering patterns across the SaaS platform
• Architect and implement scalable, production-grade ETL/ELT pipelines across Azure and AWS environments
• Build distributed data processing solutions using Python and PySpark on Databricks
• Develop batch and near real-time ingestion pipelines integrating third-party clinical systems, healthcare APIs, and external enterprise platforms
• Design secure data integration patterns (REST APIs, SFTP, event-driven ingestion, webhooks) ensuring compliance and data integrity
• Work closely with Software Engineers to embed data services directly into the SaaS product architecture
• Contribute to backend system design discussions to ensure data layer scalability, observability, and performance
• Implement CI/CD pipelines using Azure DevOps, Git, and Azure Pipelines for automated deployment and testing of data workloads
• Apply infrastructure-as-code and environment management best practices across Azure and AWS
• Optimise Spark jobs, cluster configurations, and storage strategies for performance and cost efficiency
• Design and maintain robust data models, including dimensional models and SaaS-oriented data schemas
• Implement data validation, monitoring, and alerting to ensure pipeline reliability and production stability
• Provide technical mentorship and enforce engineering standards across the analytics and data engineering team

About you

• Strong hands-on experience with Azure services including Databricks, Azure Data Factory, Azure SQL, Azure Storage, and Azure DevOps
• Practical experience with AWS services relevant to modern data platforms (S3, Lambda, RDS, Glue, IAM, etc.)
• Advanced proficiency in Python, SQL, and PySpark for large-scale distributed data processing
• Deep experience configuring and managing Databricks clusters for scalable big data workloads
• Experience building production-ready data pipelines in a SaaS or product-led engineering environment
• Strong understanding of cloud-native data architecture, including data lakes, lakehouse architecture, and modular pipeline design
• Experience integrating with third-party systems via APIs and secure data exchange mechanisms
• Exposure to healthcare or regulated data environments, including handling sensitive data securely
• Strong knowledge of data modelling, metadata management, and data governance principles
• Experience implementing automated testing frameworks for data pipelines
• Solid understanding of DevOps practices including Git workflows, branching strategies, and CI/CD automation
• Degree in Computer Science, Engineering, Data Science, or related technical field