Data Analytics Engineering Manager

hipagesgroup; Sydney; Sur place; AUD 180 000 - 240 000; Plein temps

Avantages offerts par ce poste

Hybrid working model

Employee Share Program

Cross-functional collaboration

Résumé du poste

hipages is seeking a senior Analytics Engineer to lead a craft pod of analytics engineers and scale our data architecture using dbt, Databricks and Tableau. You will design end-to-end data models, data products and self-serve analytics for the business.

You'll mentor others, establish standards, and collaborate with Product, Engineering, Marketing and Commercial teams to deliver high-impact insights and reliable data platforms.

Qualifications

• Bachelor's degree or equivalent in a quantitative field.
• Advanced SQL with scalable data architectures (Kimball, Data Vault).
• Hands-on mastery of dbt and cloud platforms (AWS, GCP, Azure).
• Experience defining/enforcing data contracts to manage interfaces.
• Experience governing semantic layers in Tableau or similar BI tools.

Responsabilités

• Lead, mentor, and line-manage a team of Analytics Engineers.
• Set standards for reproducibility, observability, and data handbook.
• Educate teams on analytics engineering best practices and self-serve.
• Advocate for dbt and guide Databricks/Tableau BI landscape.
• Design scalable data models and enforce data contracts.
• Deliver reliable data products with CI/CD and testing.

Connaissances

Advanced SQL

dbt (Data Build Tool)

Cloud platforms (AWS/GCP/Azure)

Data Contracts

Semantic layers (Tableau)

Formation

Bachelor's degree in Computer Science / Statistics / Mathematics / related quantitative field

Outils

Databricks

Tableau

SQL

Description du poste

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 “good” 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