Data Engineer

About the role

The Data Engineer Opportunity at Kogan.com represents a pivotal role within a pioneering Australian eCommerce environment that serves millions of users on a daily basis. You will own the design and operation of the critical data and machine learning pipelines that power decision-making across Marketing, Purchasing, Logistics, and Finance departments. This position demands genuine ownership where you deploy updates to production frequently, often on a daily basis, within a fast-moving engineering culture. Artificial Intelligence forms an integral component of these modern workflows, and you will utilize AI tools integrated into contemporary development practices. Your core mission is to ensure that insight travels smoothly from raw events to board-ready dashboards without ever compromising on reliability. You will play a key role in enabling data-driven confidence across the entire organization through robust pipeline construction. This role is specifically for individuals who wish to contribute to that high-impact environment and shape the data infrastructure of a major online retailer.

Key facts

What you'll do

• Design intake schemas for marketing and logistics feeds while enforcing strict quality gates to ensure the integrity of all incoming data.
• Construct analytical tables and machine learning features that empower purchasing and finance teams to execute confident and precise queries.
• Refine pipeline behavior by tracing errors and adjusting logic meticulously, ensuring that existing reports remain completely undisturbed.
• Coordinate with stakeholders consistently to align metric definitions and prevent any conflicting interpretations of the data.
• Enable shipping decisions by maintaining dashboards that accurately reflect current inventory levels and dynamic pricing states in real time.
• Form partnerships with platform teams to standardize connection methods and monitoring practices across the broader infrastructure.
• Guard data reliability through comprehensive tests that validate formats, acceptable ranges, and complex dependencies across all pipelines, which is a non-negotiable duty.
• Streamline operations by automating routine, repetitive tasks and meticulously documenting every step for future engineers to follow easily.
• Trace and resolve data quality issues by analyzing structured logs, metrics, and traces to diagnose problems swiftly in production environments.
• Optimize query performance and data models to ensure that analytical workloads run efficiently and cost-effectively within the cloud environment.
• Collaborate closely with data scientists to translate experimental models into stable, production-ready features that can be consumed reliably.
• Implement monitoring and alerting strategies to detect anomalies in data flows before they impact downstream business decisions.
• Maintain documentation that outlines data architecture, pipeline workflows, and operational runbooks to support long-term maintainability.
• Contribute to code reviews and knowledge-sharing sessions to uphold high engineering standards and promote best practices across the team.

Requirements

• Bring three to five years of hands-on experience building data platforms within a professional setting to the position.
• Write queries that join multiple sources while optimizing them rigorously for both performance and clarity, as this ability is mandatory.
• Understand how machine learning models consume transformed features and maintain the commitment to keep those structures stable and predictable.
• Work comfortably with structured logs, metrics, and traces to diagnose production issues efficiently and effectively.
• Communicate clearly in writing and collaborate effectively with cross-functional partners from diverse departments.
• Meet the hard bar of reliability, ensuring that data pipelines are robust, maintainable, and secure under all circumstances.
• Demonstrate a strong sense of ownership and discipline in problem-solving, aligning with the company's focus on clear product decisions and reliable execution.
• Operate within an environment where data, user feedback, and cross-functional input are used continuously to refine details and improve results.

Nice to have

Previous experience with marketing attribution or purchasing analytics is helpful but remains optional for this opportunity, providing an edge but not a requirement.

Practical notes

Candidates are advised to ensure their documentation and application details are current before proceeding. This ensures that all information remains current and accurate for the recruitment process.