Senior Backend Engineer (Big Data)

Binance; Darwin; Remote; AUD 120,000 - 160,000; Full time

Benefits offered by this job

Learning budget

Performance bonuses

Wellbeing support

Remote-first organisation

Job summary

Halo Labs is a future-focused, end-to-end data solutions firm transforming tomorrow today. We build secure data systems powering SaaS platforms, with cloud-first architectures across Azure and AWS.

You will develop production-grade data pipelines, optimise Spark workloads, and contribute to scalable, observable data services within a dynamic team. Join a remote-first organisation that enables travel to client sites in Australia, with learning budgets, bonuses, and wellbeing support.

Qualifications

• Demonstrated experience developing ETL/ELT processes for complex and/or large data movement, transformation and/or visualisations, particularly in a cloud environment.
• Experience preparing data optimised for query performance in cloud compute engines (Spark, Azure SQL, graph databases).
• Experience working with Engineering, Storage and Analytics services in cloud infrastructure.

Responsibilities

• 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

Skills

ETL/ELT in cloud

Query performance optimization

Cloud infrastructure analytics

Education

Degree in Computer Science/Engineering/Data Science

Tools

Azure Data Factory

Databricks

Data Lake

Lakehouse

Job description

1. Demonstrated experience developing ETL/ELT processes for complex and/or large data movement, transformation and/or visualisations, particularly in a cloud environment. 50%
2. Experience preparing data optimised for query performance in cloud computed engines. E.g. Distributed computing engines (Spark) Graph Databases Azure SQL. 25%
3. Experience working with Engineering, Storage and Analytics services in cloud infrastructure. 25%

1. Experience working with Azure Data Factory and Databricks.
2. Experience/Knowledge of working with Data Lake and Lakehouses

Job Description

Australian Citizens residing in Australia only respond. must hold a valid Baseline security clearance or be willing and eligible to obtain one.

Essential criteria

1. 1. Demonstrated experience developing ETL/ELT processes for complex and/or large data movement, transformation and/or visualisations, particularly in a cloud environment. 50%
2. 2. Experience preparing data optimised for query performance in cloud computed engines. E.g. Distributed computing engines (Spark) Graph Databases Azure SQL. 25%
3. 3. Experience working with Engineering, Storage and Analytics services in cloud infrastructure. 25%

Desirable criteria

1. 1. Experience working with Azure Data Factory and Databricks.
2. 2. Experience/Knowledge of working with Data Lake and Lakehouses

Required Skills

Spark Storage Analytics Infrastructure Databases Security SQL Engineering

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

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