Want to be a part of Asia Pacific & Middle East's (APME) largest, most innovative, and rapidly growing data centre company?
AirTrunk is a technology company with a powerful purpose - to scale and sustain the relentless growth of the region’s digital future. We do this by continuously redefining and delivering hyperscale data centres that meet the needs of our customers - the world’s most transformational companies. And we’re doing so sustainably, for today and tomorrow.
Having opened Australia’s first and largest hyperscale data centres in 2017, we set our eyes on rapid expansion and now operate a platform of hyperscale data centres across the APME region. With backing from our investors, including Blackstone, this is just the beginning…
Come join the A-Team at AirTrunk, where the cloud meets the ground.
A Snapshot
As an Analytics Engineer at AirTrunk, you will help transform raw data into trusted, analytics-ready datasets that power reporting, dashboards, and business insights. You’ll work closely with data engineers, analysts, and business stakeholders to support data modelling, build transformation workflows, and contribute to high-quality, well-governed data products. Using tools like dbt, SQL, and modern cloud platforms, you’ll help deliver scalable and reliable data solutions while continuing to grow your skills in analytics engineering. This role is ideal for someone with a strong foundation in data who is looking to deepen their experience in modelling, transformation, and delivering impactful data products.
Your Day to Day
Data Modelling & Transformation
- Build and maintain dbt-based transformation workflows to create clean, reliable, and reusable datasets.
- Support the development of semantic models and curated data layers for BI and analytics use cases.
- Assist in implementing data tests, documentation, and basic data contracts to ensure quality and usability.
- Help optimise queries and transformations for performance and cost efficiency.
- Work with stakeholders to understand data requirements and support delivery of analytics-ready datasets.
Data Product Development
- Support the development and maintenance of data pipelines that feed dashboards, reports, and analytics use cases.
- Assist in building near real-time or batch data flows using tools such as Kafka, Azure Event Hubs, or Delta Live Tables.
- Help ensure consistency and reliability of datasets across different consumers and use cases.
- Contribute to the development of data products that enable self-service analytics.
CI/CD & Automation
- Contribute to CI/CD pipelines for analytics workflows, supporting testing and deployment processes.
- Follow established DevOps practices including version control, branching strategies, and environment management.
- Assist in automating repetitive tasks to improve efficiency and reduce manual effort.
- Participate in code reviews and adopt team standards for quality and consistency.
Governance & Observability
- Support the implementation of data quality checks, validation rules, and monitoring for analytics datasets.
- Assist in maintaining documentation, metadata, and lineage for datasets and transformations.
- Work with data engineers to align on governance standards and platform practices.
- Help identify and troubleshoot data quality or pipeline issues.
Collaboration & Mentorship
- Collaborate with data engineers, analysts, and business stakeholders to deliver reliable analytics solutions.
- Translate business requirements into technical tasks with guidance from senior team members.
- Actively learn from peers and contribute to team knowledge sharing and continuous improvement.
- Participate in AirTrunk’s Data & AI community of practice.
Requirements
- 3–5 years’ experience in analytics engineering, data engineering, or a related data-focused role.
- Hands-on experience with dbt or similar tools for data transformation and modelling.
- Strong SQL skills and experience working with cloud-based data platforms such as Azure, Snowflake, or Databricks.
- Familiarity with building and maintaining data pipelines for analytics and reporting use cases.
- Basic understanding of data modelling concepts, semantic layers, and analytics workflows.
- Exposure to CI/CD pipelines, Git, and DevOps practices for analytics engineering is beneficial.
- Understanding of data quality, testing, and documentation practices.
- Exposure to BI tools and reporting concepts, with an understanding of how data supports business decision-making.
- Strong problem-solving skills, attention to detail, and a willingness to learn and grow.
- Good communication and collaboration skills, with the ability to work effectively in cross-functional teams.
