About the role:

You’ll play a crucial role in supporting the hipages marketplace by developing our analytics engineering capabilities. Working alongside a talented data team to create immense value for the business and contribute to the development and execution of hipages' commercial strategy.

Your experience and expertise will help us discover new ways of operating in the world of data modelling and business intelligence. You’ll design and support the deployment of end-to-end data models, data products as well as develop and maintain internal analytics capabilities within our data platform.

This role is vital in scaling our current data architecture and capabilities on DBT, Databricks and Tableau to enable self‑service analytics. This role will be reporting into the Head of Commercial and Data Analytics giving that mentorship and executive buy‑in to ensure this role is a success.

This is a senior, hands‑on role leading a craft pod of three analytics engineers, yet setting direction across a much wider community, acting as the technical authority on what “positive” data design looks like.

Why join our Data team?

- Hybrid working model
- In-house Talent Development team to prioritise personal and career growth
- Competitive salary, benefits and perks, plus equity via our Employee Share Program
- Cross-functional collaboration across Product, Engineering, Marketing and Commercial teams
- Hands‑on learning opportunities and workshops for continuous upskilling

How you will add value:

Leadership & People Management

Lead, mentor, and line‑manage a team of 2 Analytics Engineers, fostering a culture of technical excellence, continuous learning,



and open collaboration.

Shape analytics engineering practices across hipages, establishing standards for reproducibility, observability, style guides, and contributions to our data handbook.

Educate data teams and broader business stakeholders on analytics engineering best practices and self‑serve capabilities.

Architectural & Technical Execution

Serve as the primary advocate for dbt while providing technical leadership throughout the Databricks and Tableau BI landscape.

Architect, design, and implement scalable, high‑performance data models in dbt and Databricks in close collaboration with Data Analysts and Data Engineers.

Define, establish, and enforce data contracts between software engineering producers and downstream data models to eliminate breaking upstream schema changes.

Delivery & Engineering Excellence

Apply modern software engineering best practices (CI/CD, Git version control, automated testing) to iteratively deliver reliable data products and platform capabilities.

Drive end‑to‑end data quality, governance, SLAs, and consistency across our analytics and data science ecosystem.

Identify operational bottlenecks, optimize query execution, and streamline data pipelines to enhance overall system performance.





Oversee comprehensive documentation of data architecture, pipeline lineage, and analytical workflows.

About you:

Technical & Architectural

- Bachelor's degree in Computer Science, Statistics, Mathematics, Physics, Economics or a related quantitative qualification or relevant equivalent experience
- Advanced SQL with deep experience designing and building scalable data architectures (Kimball dimensional modeling, Data Vault, etc.)
- Hands‑on mastery of dbt (Data Build Tool) and strong experience with enterprise cloud platforms (AWS, GCP, or Azure)
- Hands‑on experience defining, enforcing, or implementing Data Contracts to manage producer‑consumer interfaces
- Proven experience developing and governing semantic layers in AI and BI tools like Tableau

Leadership & Strategic Execution

- Proven capability to manage direct reports, work independently, and drive cross‑functional alignment with excellent communication and conflict‑resolution skills.
- Comfortable navigating ambiguity, managing priorities, and delivering iterative value in a fast‑paced environment.
- Passionate about data culture, with a proven ability to translate complex data structures into clear, actionable narratives for technical and non‑technical stakeholders.

Nice to have

- Exposure to two‑sided marketplace dynamics, network metrics, or high‑velocity event‑driven data ecosystems
- Experience partnering with Data Science teams to build feature stores, orchestrate pipelines (e.g., Airflow, Databricks Workflows), or support ML model deployments

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📌 Data Analytics Engineering Manager (New South Wales)
🏢 hipagesgroup
📍 New South Wales