Job Description
About Us
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 recent 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
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