Engineer - Data

Why this role exists:

The Engineer (Data) develops and maintains the robust data pipelines and transformations required to power Officeworks enterprise data platform and emerging AI capabilities. The role supports the Technology function by integrating diverse data sources into Snowflake, ensuring high data quality and system reliability to enable a self-serve analytics model. It plays an important part in the execution of the enterprise data strategy, helping Officeworks to democratise data access and drive informed decision-making across the business.

Where you will make a difference:

In this role you will:

Data Engineering & Pipeline Development

• Develop, maintain, and optimise scalable data pipelines to support business-critical reporting and analytics.
• Integrate complex data sets from various sources, including SAP Datasphere, Salesforce, and Adobe, into Snowflake.
• Support the implementation of data modelling and transformation logic to ensure data is fit for purpose.

Operational Monitoring & Reliability

• Monitor pipeline health and proactively address failures to maintain data integrity and availability.
• Troubleshoot and resolve data-related issues, ensuring minimal disruption to downstream consumers.
• Contribute to the improvement of data monitoring frameworks and alerting systems.

Process & Continuous Improvement

• Drive process improvement initiatives within the data engineering lifecycle to enhance delivery speed and quality.
• Identify opportunities for automation within manual data workflows and transformation steps.
• Regularly review and refine existing pipelines to ensure they align with evolving performance standards and best practices.

Engineering Excellence & CI/CD

• Contribute to and maintain CI/CD workflows to ensure seamless and reliable code deployment.
• Adhere to engineering standards for documentation, version control (GitHub), and code quality.
• Collaborate with Quality Analysts to ensure data work meets rigorous testing and validation standards.

Who you will be working with:

• Data & AI Team: Partner with the Data Platform Manager, Technical Associate Managers, and peer Engineers to deliver integrated data solutions.
• Analytics Teams: Collaborate with Data Scientists and Analysts to provide the foundational data structures required for machine learning and AI initiatives.
• Architectural Leads: Work with the Data Architect to ensure pipelines align with the enterprise data strategy and Snowflake platform standards.
• Functional Business Partners: Engage with stakeholders across Salesforce, Adobe, and SAP workstreams to understand source system complexities.

What success looks like:

• Pipeline Reliability: Data pipelines are highly available, with minimal downtime and rapid resolution of identified failures.
• Data Democratisation: Successful integration of key data sources into Snowflake, enabling self-serve capabilities for business users.
• Continuous Improvement: Demonstrable improvements in pipeline performance, automation levels, and engineering process efficiency.
• Delivery Quality: Consistent delivery of data transformations that meet technical specifications and business requirements within agreed timeframes.

How you will lead:

Individual Contributor

• Lives our Officeworks values and behaviours
• Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behaviour
• Operates in line with applicable Officeworks company policies and Code of Conduct
• Demonstrates a strong sense of personal accountability and curiosity to learn and develop

Qualifications and work experience:

Essential

• Education: A Bachelors degree in Computer Science, Information Technology, or a related field.
• Experience: 3-5 years of experience in data engineering or data development roles within a complex corporate environment.
• Technical Skills: Proficiency in GitHub for version control and experience developing within the Snowflake data platform.
• Data Integration: Proven experience integrating data from large-scale enterprise systems (e.g., SAP, Salesforce).
• Transformation Knowledge: Strong understanding of data modelling, SQL, and transformation logic.
• Adaptability: Demonstrated ability to learn new technologies and adapt to evolving data environments, specifically shifting from traditional ML to AI-ready data structures.

Preferred

• Retail Context: Experience working within the retail or omnichannel sector.
• System Exposure: Familiarity with SAP Datasphere, Adobe data structures, or Machine Learning (ML) system architectures.