Data Engineer, ANZSA

The people here at Apple don’t just create products — they create the kind of wonder that’s revolutionised entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it.

Apple’s Sales organisation generates the revenue needed to fuel our ongoing development of products and services. This in turn, enriches the lives of hundreds of millions of people around the world. Our sales team, is in many ways, the face of Apple to our largest customers.

As the Data Engineer, you will be instrumental in the design, development, and implementation of data engineering solutions for the Apple Channel Sales team in ANZSA (Australia, New Zealand and South Asia) that have direct and measurable impact on Apple Sales and its customers.

Partnering with internal and external partners you will be responsible for the data that enables our Sales Teams to make informed decisions. You will be the main architect behind our data infrastructure, ensuring that data is easily accessible, reliable, and actionable. This in turn will facilitate the automation of sales processes as well as the creation of insights, reports and AI solutions.

Key responsibilities include building, maintaining and optimising scalable data pipelines for both structured and unstructured data in production, enabling the development and deployment of AIML models. Collaborating with various cross functional, regional and global teams, you will optimise data processes, and ensure that our data practices align with industry standards, best practices and regulations.

This includes defining and disseminating global best practices, setting technical standards for data architecture, coding and and modelling conventions, and supporting data governance policies to ensure consistent data generation, processing, and reporting. You will safeguard the definitions and integrity of the business metrics our Sales team rely on.

We are looking for an exceptional individual that lives at the intersection of development, operations, data, and systems engineering to build solutions for scalable data transformation and delivery.

Minimum Qualifications

• 5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.
• Development experience with cloud database environments like Snowflake, Dremio, Redshift or Databricks.
• Experience deploying and operating data pipelines and services on cloud platforms, including containerisation, CI/CD, scheduling and production monitoring.
• Proficiency in programming languages like SQL, Python, Java or R
• Architecting and developing data pipelines through ETL tools, API integrations with systematic and cloud based source systems.
• Strong understanding of data modelling, data warehousing, and ETL concepts
• Familiarity with AI/ML model development lifecycle and data needs for training and deployment.
• Experience and understanding of unstructured data, API development and basic frontend development.
• Solid understanding of data governance frameworks, secure secrets management, and handling of sensitive data.

Preferred Qualifications

• Experience articulating and translating business questions into data solutions and proven ability to lead development projects from start to finish.
• Able to balance competing priorities, long-term projects, and ad hoc requirements.
• Demonstrated ability to positively influence and collaborate with people across all functional areas of an organisation.
• Demonstrated ability to achieve strategic goals in an innovative and fast-paced environment.
• Outstanding ability to problem solve, develop creative solutions, and demonstrate resourcefulness, while maintaining extreme attention to detail.
• Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
• Excellent communication skills, able to inspire non-technical colleagues on value proposition and impact of data governance.