Data Engineer

Sirius.; Sydney; On-site; AUD 130,000 - 180,000; Full time

Job summary

Sirius. in Sydney leads a major Australian retail network's data initiative, building data & AI capabilities from the ground up. This hands-on Data Engineer role sits in a new Data & Insights team, delivering ELT pipelines, models, and analytic foundations for commercial decisions across the national footprint.

You will work with multi-site POS data, ecommerce, and marketing data, bringing them into a governed Snowflake environment and enabling analytics-ready datasets for business decisions.

Qualifications

• 5–8 years of data engineering experience with pipeline delivery and data quality focus.
• Strong SQL and Python; production-grade coding and Snowpark is a plus.
• Snowflake and dbt experience with testing and documentation.
• Master data management or customer data matching experience.
• Experience with Azure Data Factory and ADLS for ingestion/ orchestration.
• Hands-on iPaaS experience and integrations delivery.
• Data quality profiling, validation rules, and DQ monitoring experience.
• Dimensional modeling knowledge (star schemas, Kimball).
• BI data structuring for Power BI; familiar with legacy sources.

Responsibilities

• Build and maintain ELT pipelines from source systems into Snowflake.
• Develop dbt models and adhere to medallion architecture.
• Design dimensional models to support reporting and AI use-cases.
• Support ingestion via Azure Data Factory and ADLS; evaluate iPaaS tools.
• Contribute to AI initiatives and agentic AI workflows.
• Troubleshoot data quality issues and implement validation logic.
• Ensure well-modelled, high-performance datasets for Power BI.
• Configure iPaaS integrations to automate data movement.
• Apply CI/CD, version control, and testing on the data platform.
• Collaborate with data leadership and stakeholders to translate requirements into data products.

Skills

SQL

Python

Snowflake

dbt

Master data management

Azure Data Factory

ADLS

iPaaS

Data quality

Dimensional modelling

Power BI

Git

CI/CD

Agile

Tools

Snowflake

dbt

Azure Data Factory

Power BI

Git

Job description

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.