Job Description
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About Us
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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.
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Why work at Halo Labs?
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Leading Innovation: We don't just solve problems; we illuminate a never-ending stream of innovation.
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Exceptional Perks: Enjoy outstanding perks like a dedicated learning budget, performance bonuses, and comprehensive wellbeing support.
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Remote-First Organisation: Experience the perks of remote work while having the flexibility to travel to client locations throughout Australia.
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Inclusive and Engaging: Celebrate diversity in a welcoming space that thrives on new ideas and open conversations, all in a respectful environment.
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Inspiring Origins: With a compelling founder story, we are a customer-focused, culture-first organisation.
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About the role
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• Design, develop, and maintain reusable in-house PySpark frameworks to enforce standardised data engineering patterns across the SaaS platform
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• Architect and implement scalable, production-grade ETL/ELT pipelines across Azure and AWS environments
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• Build distributed data processing solutions using Python and PySpark on Databricks
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• Develop batch and near real-time ingestion pipelines integrating third-party clinical systems, healthcare APIs, and external enterprise platforms
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• Design secure data integration patterns (REST APIs, SFTP, event-driven ingestion, webhooks) ensuring compliance and data integrity
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• Work closely with Software Engineers to embed data services directly into the SaaS product architecture
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• Contribute to backend system design discussions to ensure data layer scalability, observability, and performance
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• Implement CI/CD pipelines using Azure DevOps, Git,



and Azure Pipelines for automated deployment and testing of data workloads
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• Apply infrastructure-as-code and environment management best practices across Azure and AWS
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• Optimise Spark jobs, cluster configurations, and storage strategies for performance and cost efficiency
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• Design and maintain robust data models, including dimensional models and SaaS-oriented data schemas
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• Implement data validation, monitoring, and alerting to ensure pipeline reliability and production stability
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• Provide technical mentorship and enforce engineering standards across the analytics and data engineering team
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About you
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• Robust hands-on experience with Azure services including Databricks, Azure Data Factory, Azure SQL, Azure Storage, and Azure DevOps
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• Practical experience with AWS services relevant to modern data platforms (S3, Lambda, RDS, Glue, IAM, etc.)
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• Advanced proficiency in Python, SQL, and PySpark for large-scale distributed data processing
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• Deep experience configuring and managing Databricks clusters for scalable big data workloads
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• Experience building production-ready data pipelines in a SaaS or product-led engineering environment
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• Strong understanding of cloud-native data architecture, including data lakes, lakehouse architecture, and modular pipeline design
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• Experience integrating with third-party systems via APIs and secure data exchange mechanisms
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• Exposure to healthcare or regulated data environments, including handling sensitive data securely
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• Strong knowledge of data modelling, metadata management, and data governance principles
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• Experience implementing automated testing frameworks for data pipelines
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• Solid understanding of DevOps practices including Git workflows, branching strategies, and CI/CD automation
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• Degree in Computer Science, Engineering, Data Science, or related technical field
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📌 Data Engineer - Remote (Melbourne)
🏢 Halo Labs Pty
📍 Melbourne