Data Engineer

Kogan.com; City of Melbourne; On-site; AUD 120,000 - 160,000; Full time

Benefits offered by this job

$1,000 learning budget

Kogan First membership

Team discounts

Health and wellbeing initiatives

Lunch & Learns

Hackathons

Referral bonuses

Volunteering opportunities

Regular team events

Job summary

Kogan.com is building a fast-moving data-driven engineering team in Melbourne to design and run data and ML pipelines powering Marketing, Purchasing, Logistics and Finance decisions.

As a Data Engineer you’ll craft scalable ETL/ELT pipelines, model data in BigQuery or Snowflake, and support ML workflows from feature generation to deployment. You’ll work with Airflow, dbt, AWS Glue, and Google Cloud, embracing CI/CD and best practices.

Qualifications

• Strong SQL skills for commercial-scale data products.
• Experience building and maintaining ETL/ELT pipelines.
• Experience with ML data workflows and features.
• Cloud data platforms, preferably GCP.
• Familiarity with data governance and quality practices.
• Proficient in Python for data engineering tasks.

Responsibilities

• Design and run data and ML pipelines for Marketing, Purchasing, Logistics and Finance.
• Develop scalable ETL/ELT pipelines handling 10M+ daily events.
• Model data in environments like BigQuery or Snowflake for analytics and ML training.
• Support ML workflows and feature inputs for models.
• Develop and refine ML models for business use cases such as sentiment, churn, demand forecasting.
• Establish MLOps pipelines for deployment and monitoring in production.
• Integrate with internal APIs and third-party tools while ensuring data integrity.
• Uphold data governance, quality, and documentation as a source of truth.

Skills

SQL Foundations

Python Proficiency

ML Engineering Exposure

Cloud Experience (GCP)

Problem-Solving Mindset

Software Engineering Practices

Tools

Airflow

dbt

AWS Glue

Docker

Git

CI/CD

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 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 strong 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.