Senior Python Data Scraping Engineer (Freelance) in Gold Coast

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 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.