Data Engineer

About Visy:

Visy is a family-owned Australian business and a global pioneer in sustainable packaging, recycling and logistics. They operate across 150 sites globally, including operations in Asia, Europe, and the USA, supported by a dedicated workforce of over 7,000 employees. It is Australia and New Zealand's largest manufacturer of food and beverage packaging, made from household recycling. As Australia's largest recycler, it processes 40% of Australian households recycling. Visy also supports customers with logistics, packaging supplies, point of sale displays and more. At Visy India, their technology hub in Hyderabad, they are expanding their technical capabilities to support their global business.

Position Summary:

The Data Engineer is responsible for designing, building and supporting data solutions that enable reporting, analytics and business decision-making across Visy.

Working within the Data Platform team, the role develops and maintains data pipelines, integrations and cloud-based data platforms, ensuring data is accurate, secure and accessible. The Data Engineer partners with business and technology stakeholders to deliver reliable data solutions and drive continuous improvement across Visy's data environment.

Key Responsibilities:

Data Solutions & Delivery:

• Design, build and maintain data pipelines, integrations and data platforms.
• Develop reliable, scalable and secure data solutions aligned with enterprise standards and architecture.
• Support business reporting, analytics and operational data requirements.

Data Quality, Governance & Security:

• Ensure data quality, integrity, security and governance requirements are embedded within data solutions.
• Monitor and resolve data issues to maintain the availability and reliability of critical data assets.

Support & Continuous Improvement:

• Provide operational support for data platforms and integrations.
• Troubleshoot and resolve data and platform issues.
• Optimise platform performance, reliability and operational efficiency.
• Identify and implement opportunities for automation and continuous improvement.

Stakeholder Collaboration:

• Work closely with business stakeholders, analysts, architects and technology teams to understand requirements and deliver fit-for-purpose solutions.
• Maintain technical documentation and contribute to project delivery and knowledge sharing.

Key Challenges:

• Delivering reliable data solutions across multiple systems and business functions.
• Balancing project delivery, operational support and continuous improvement.
• Maintaining data quality, security and governance within a complex technology environment.
• Supporting changing business requirements while maintaining platform stability and performance.

Key Stakeholders:

Internal:

• Industrial & Digital Technologies teams
• Business stakeholders and functional leaders
• Enterprise Architecture
• Cyber Security
• Project and Delivery teams

Direct Reports: None

Mandatory Experience & Qualifications:

• Degree in Computer Science, Information Technology, Engineering or a related discipline.
• 3–5 years of experience in Data Engineering, Data Integration or a similar role.
• Experience designing and supporting cloud-based data platforms and data pipelines.
• Strong SQL skills.
• Strong data modelling experience.
• Experience with ETL/ELT processes.
• Good understanding of data warehousing concepts.
• Strong analytical and problem-solving skills.
• Strong stakeholder engagement and communication skills.

Technology Environment:

AWS:

• Amazon S3
• AWS Lambda
• AWS Glue
• Amazon Redshift
• AWS CodePipeline

Languages:

• SQL
• Python

Data Engineering:

• ETL/ELT
• Data Warehousing
• Star Schema
• Snowflake Schema
• Dimensional Modelling

Desirable Skills:

• Experience with AWS data services and cloud-native data platforms.
• Experience with CI/CD and Infrastructure as Code.
• Knowledge of SAP data domains, particularly Finance, Sales and Materials Management.
• Relevant cloud or data engineering certifications.

Key Skills:

• Data Engineering and Data Integration
• Data Warehousing
• Cloud-based Data Platforms
• SQL and Data Modelling
• Data Transformation
• ETL/ELT
• Data Governance, Security and Quality
• Production Support and Troubleshooting
• Automation and Continuous Improvement
• Analytical and Problem-solving skills
• Stakeholder Management and Collaboration