About the role
We are partnering with a major Australian multi‑site retail and franchise network as they build their data and AI capabilities from the ground up. This is a hands‑on Data Engineer role within a newly formed Data & Insights team, responsible for building and maintaining the pipelines, models, and reporting foundations that power commercial decision‑making across a massive national footprint.
You’ll work directly within the central Digital, Data, and Technology team, helping bring data from multi‑site point‑of‑sale systems, ecommerce, and marketing platforms into a governed Snowflake environment, turning raw information into trusted, analytics‑ready datasets for the wider business.
What you’ll be doing
- Build and maintain ELT pipelines bringing data from source systems (including legacy ERPs/iSeries, enterprise ecommerce, and marketing platforms) into Snowflake.
- Develop and maintain dbt models following a medallion architecture and strict modelling conventions.
- Design and build dimensional models (e.g., customer, product, location, and inventory dimensions, plus related fact tables) to support reliable reporting and future AI use‑cases.
- Work with Azure Data Factory and ADLS for ingestion pipelines and support the evaluation and implementation of iPaaS tooling.
- Contribute to AI initiatives across the business, including the integration of LLM solutions (Claude, ChatGPT, Gemini) and support the design of agentic AI workflows that extend platform capabilities.
- Troubleshoot data quality issues at the source (nulls, inconsistent keys, duplicates) and build robust validation and cleansing logic.
- Support Power BI reporting by ensuring datasets are well‑modelled, highly performant, and documented.
- Configure iPaaS integrations to connect operational systems, automate data movement, and reduce manual handoffs.
- Apply sound CI/CD, version control, and testing practices to the data platform.
- Partner with data leadership, BAs, and business stakeholders across ecommerce, marketing, and retail operations to translate complex requirements into reliable data products.
What we’re looking for
- 5–8 years of experience in data engineering, with demonstrable depth across pipeline delivery, data quality, and master data disciplines.
- Strong SQL and Python skills: you write clean, production‑grade code and are comfortable using Python via Snowpark for complex logic (entity resolution, probabilistic matching, DQ profiling).
- Solid Snowflake and dbt experience, including testing frameworks and documentation.
- Proven experience with master data management, entity resolution, or customer data matching, including deterministic and probabilistic deduplication approaches.
- Experience building and governing reference data sets (hierarchies, lookups, classification schemes).
- Practical experience with Azure Data Factory and ADLS for ingestion and orchestration.
- Hands‑on iPaaS experience: you have actively configured and delivered integrations.
- Deep familiarity with data quality profiling, validation rule design, and DQ monitoring within a layered warehouse architecture.
- Strong understanding of dimensional modelling (star schemas, Kimball methodology) and ELT design patterns.
- Experience structuring efficient, high‑performing datasets for BI tools (Power BI preferred).
- Comfort working with legacy/messy source systems and confidence communicating trade‑offs to non‑technical stakeholders.
- Familiarity with Git, CI/CD practices, and agile delivery.
- A delivery‑first mindset: you scope clearly, build incrementally, and thrive in a small, fast‑moving team where the platform is still being established.
Nice to have
- Experience in complex multi‑site retail, high‑value goods, or large‑scale ecommerce data environments.
- Exposure to PIM, ERP, or large‑scale retail/franchise management systems.
- Experience extracting data from legacy ERP environments (e.g., AS400/iSeries).
- A strong interest in data governance and emerging AI practices.