Kogan.com is a pioneer of Australian eCommerce, and the software we build is used by millions of customers every day. You'll join a fast-moving engineering team with real ownership, shipping to production daily and using AI as part of how we work.
As a Data Engineer you'll design and run the data and ML pipelines that let teams across Marketing, Purchasing, Logistics and Finance make confident, data-driven decisions.
What you'll do:
Scalable Pipeline Development:
Design and maintain ETL/ELT pipelines capable of handling
10M+ daily events
and large-scale data transfers across our platforms.
Data Modeling:
Develop and optimize data models in environments like
BigQuery or Snowflake
to ensure high performance for both analytics and ML training sets with optimal cost
Support ML Workflows:
Build the underlying features and data inputs required for Machine Learning models
Develop and refine ML models
for practical business use cases, such as customer sentiment, churn prediction or demand forecasting
MLOps Integration:
Establish and maintain
MLOps pipelines
to help automate the deployment and monitoring of models in production.
System Integration:
Work with internal APIs and third-party tools to ingest data efficiently while maintaining strict data integrity.
Governance & Quality:
Implement best practices for data quality, security, and documentation to ensure our data remains a "source of truth."
Development according to
software engineering best practices
(Git, CI/CD, trunk based development, tests)
AI Collaboration:
Contribute to experiments with AI and LLMs to assess how they can be practically applied to solve business problems.
What you'll need:
Strong SQL Foundations:
Solid experience writing and optimizing SQL for




commercial-scale products
(e.g., handling millions of rows and complex joins efficiently).
Pipeline Orchestration:
Proven experience using tools like
Airflow, dbt, or AWS Glue
to manage and monitor production-grade data workflows.
Python Proficiency:
Strong Python skills for data transformation, scripting and interacting with various data sources.
ML Engineering Exposure:
Practical experience building the data infrastructure that supports machine learning, including data preprocessing and model deployment pipelines. Experience with machine learning models development
Cloud Experience:
Hands-on experience with cloud data platforms, with a robust preference for
GCP
.
Software Best Practices:
Familiarity with Git, CI/CD, and basic containerization (Docker) to ensure code quality and deployment reliability.
Problem-Solving Mindset:
A practical approach to engineering that balances the need for speed with long-term system stability.
Why Kogan.com?
Work on machine learning , data and AI products that are used by millions of customers and have a measurable impact on the business.
Own problems end to end, from experimentation and modelling through to deployment and optimisation in production.
Join a highly capable engineering team that values autonomy, fast execution and practical innovation.
Help shape the future of AI, machine learning and eCommerce at one of Australia's leading technology businesses.
Receive a $1,000 annual learning budget to invest in your growth and development.
Enjoy a range of benefits including a complimentary Kogan First membership, team discounts, health and wellbeing initiatives, Lunch & Learns, hackathons, referral bonuses, volunteering opportunities and regular team events.
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📌 Data Engineer (Melbourne)
🏢 Kogan.com
📍 Melbourne