Data Engineer

What you'll do:

• Scalable Pipeline Development: Design and maintain ETL/ELT pipelines capable of handling10M+ daily events and large-scale data transfers across our platforms.

• Data Modeling: Develop and optimize data models in environments likeBigQuery 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 maintainMLOps 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 tosoftware 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 forcommercial-scale products (e.g., handling millions of rows and complex joins efficiently).

• Pipeline Orchestration: Proven experience using tools likeAirflow, 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 strong preference forGCP.

• 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.

 Bonus Points

• Experience with event streaming (e.g.,Kafka, Kinesis) for real-time data needs.

• Exposure to ML platforms and tools such asSageMaker, Vertex AI, or Databricks.

• Familiarity with BI and visualization tools likeLooker or Tableau.

• An interest in eCommerce dynamics and customer behavior analytics.

Why Kogan.com?

• Our culture is unlike anywhere else and regardless of where you are in your career journey, we empower you to do your best work and have a big impact. Check us outhttps://devblog.kogan.com/ &https://goodteams.app/teams/kogan.com.
• Work with an incredible team to solve important challenges, helping to drive Australia and New Zealand's eCommerce future.
• Your role has a lot of ownership, autonomy and little red tape. You'll be empowered to achieve positive outcomes and your work will have a real impact.
• You'll be at the forefront of the eCommerce industry and be part of a company that are the Pioneers of eCommerce in Australia.
• Be an Intrepreneur, playing a hands on role in shaping our strategy at our HQ.
• Learning budget of $1000.
• A range of employee benefits such as; complimentary Kogan First Membership, team exclusive discounts, Health & Wellness program, Learning & Development and Lunch & Learns, Hackathons, Team member referral program, Company and team events and celebrations, community engagement (volunteering) and extensive career development opportunities plus loads more!