Analytics Engineer

About the role

Introduction

Xephyr builds sovereign AI agents and the data platforms that power them, for enterprises including Wesfarmers, Telstra, Bupa, Kmart, Garvan Institute and Sunnyfield. Your data doesn't move. Your governance rules apply. Every agent runs inside your environment. We build three things: the data foundations and business context agents need, custom agents trained on client data and workflows, and Xavier, our analytics agent that delivers trusted answers from data fast. If you want to get in on the ground floor of a fast-paced startup where your work has a direct impact on the success of our mission, we'd love to hear from you.

Role summary

We are looking for a mid-level Analytics Engineer to build the end-to-end pipelines that turn raw client data into trusted, decision-ready analytics. You will own the full chain: ingestion, transformation, semantic and data modelling, through to the dashboards and visualisations the business actually uses.

A defining part of this role is working directly with the business to gather requirements. You will run discovery with stakeholders to understand their use cases, the decisions they need to make, and the measures and dimensions that matter, then translate that into a well-structured semantic model. You are equally comfortable in a requirements workshop and in the warehouse, and you know that a good pipeline is only as good as the business logic it encodes.

Responsibilities and duties

- Design and build end-to-end data pipelines: ingestion, transformation, semantic and data modelling, through to the visualisation and reporting layer.

- Run requirements-gathering sessions directly with the business to capture use cases, key decisions, and the measures and dimensions that should sit in the semantic model.

- Design and maintain semantic layers and dimensional data models that support self-serve analytics and Xavier, our analytics agent.

- Build and maintain dashboards and visualisations that answer the business questions surfaced during requirements work, not just the ones that were easiest to build.

- Apply data modelling best practice: consistent naming, a business glossary, and clear metric definitions, so models are trusted and reused across engagements.

- Work across the modern data stack: ingestion and ELT tooling, orchestration, transformation frameworks (e.g. dbt), and cloud data warehouses such as Snowflake.

- Collaborate with data engineers, data scientists, delivery leads and client stakeholders to keep technical design aligned to business objectives.

- Follow and champion efficient, repeatable and scalable engineering processes (version control, testing, CI/CD, documentation).

- Contribute reusable patterns, semantic model templates and delivery accelerators back into Xephyr's IP and the Xavier product.

Ideal candidate

- 3-5 years' hands-on experience as an analytics engineer, BI developer or data engineer, with demonstrated ownership of a pipeline end to end, from ingestion through to visualisation.