Job Description

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 quick-moving engineering team with real ownership, shipping to production daily and using AI as part of how we work.

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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.

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What you'll do:

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- Scalable Pipeline Development: Design and maintain ETL/ELT pipelines capable of handling 10M+ daily events and large-scale data transfers across our platforms.
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- 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
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- Support ML Workflows: Build the underlying features and data inputs required for Machine Learning models
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- Develop and refine ML models for practical business use cases, such as customer sentiment, churn prediction or demand forecasting
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- MLOps Integration: Establish and maintain MLOps pipelines to help automate the deployment and monitoring of models in production.
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- System Integration: Work with internal APIs and third-party tools to ingest data efficiently while maintaining strict data integrity.
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- Governance & Quality: Implement best practices for data quality, security, and documentation to ensure our data remains a "source of truth."
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- Development according to software engineering best practices (Git, CI/CD, trunk based development, tests)
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- AI Collaboration: Contribute to experiments with AI and LLMs to assess how they can be practically applied to solve business problems.
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What you'll need:

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- Strong SQL Foundations:



Solid experience writing and optimizing SQL for commercial-scale products (e.g., handling millions of rows and complex joins efficiently).
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- Pipeline Orchestration: Proven experience using tools like Airflow, dbt, or AWS Glue to manage and monitor production-grade data workflows.
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- Python Proficiency: Strong Python skills for data transformation, scripting and interacting with various data sources.
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- 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
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- Cloud Experience: Hands-on experience with cloud data platforms, with a strong preference for GCP .
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- Software Best Practices: Familiarity with Git, CI/CD, and basic containerization (Docker) to ensure code quality and deployment reliability.
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- Problem-Solving Mindset: A practical approach to engineering that balances the need for speed with long-term system stability.
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Why Kogan.com?

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- Work on machine learning , data and AI products that are used by millions of customers and have a measurable impact on the business.
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- Own problems end to end, from experimentation and modelling through to deployment and optimisation in production.
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- Join a highly capable engineering team that values autonomy, fast execution and practical innovation.
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- Help shape the future of AI, machine learning and eCommerce at one of Australia's leading technology businesses.
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- Receive a $1,000 annual learning budget to invest in your growth and development.
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- 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