Data Engineer

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.