The Blackmores AI Health Lab is a greenfield initiative aimed at leveraging AI and machine learning to drive innovation, personalize customer health experiences, and support evidence-based decision-making. This senior leadership role is crucial in designing, building, and optimizing cloud-native data pipelines to ensure that AI and machine-learning models have access to clean, timely, and trusted data. The Senior Data Engineer will collaborate with various departments, including IT, data science, and business users, to achieve these goals. Curious and a proven technologist, you will ask the “Why” of users to ensure that no hidden data is left behind. Users will be able to outline the data they need from source systems (internal and external to Blackmores) and the Ai Data Engineer will build the required integration and pipelines to extract the data and land them in the desired destination. As this will be a new build of a data platform, your ability to leverage existing and introduce new best practice standards in the build will be essential.
As a purpose-led, performance-driven organisation, Blackmores Group strives to create remarkable employee experiences through supporting a diverse and inclusive culture, creating leadership that inspires high performance, and providing everyone with opportunities to achieve their professional, personal and wellbeing goals supported by our flexible work practices and family friendly policies.
Blackmores Group operates in 11 markets, with headquarters in Sydney, an international state-of-the-art manufacturing facility in Braeside, Victoria and additional offices in South East Asia and China.

QUALIFICATIONS AND SKILLS

Technical Skills: Proficiency in GCP Vertex AI, Python, Snowflake, CI/CD with GitHub Enterprise, API, and batch / real-time data experience.
Experience: 5-7+ years of experience in data engineering or a related field having worked with range of data and formats both internal and external data sets. 3yr+ experience in Agile and ML Ops workflows.
Education: Bachelor’s degree in computer science, Engineering, or a related field. Advanced degree preferred.
Soft Skills: Strong problem-solving skills, attention to detail, and the ability to work collaboratively in a team environment.
Agencies please note: this recruitment assignment is being managed directly by Blackmores Talent Acquisition team. We will reach out to our preferred agency partners if required. Your respect for this process is appreciated.

PURPOSE OF ROLE:

Design, build, and optimize Blackmores’ cloud-native data pipelines to ensure AI and machine-learning models have clean, timely, and trusted data.

RESPONSIBILITIES INCLUDE:

• Strategy / Planning: Develop and implement strategies for data pipeline architecture and optimization. Experience with design patterns for data engineering. Effectively manage the demand pipeline. Uses Agile and MVP approaches to help end users understand their information needs more clearly so the right data can be acquired.
• Execution / Delivery: Design, build, and optimize scalable, secure ETL/ELT workflows that deliver clean, model-ready data to AI/ML services with near-real-time latency. CI/CD experience to continuously improve the capability and diversity of data that directly supports and influences business functions
• Data Governance: Safeguard data quality, lineage, and compliance with industry standards and regulations. Proven experience in establishing data quality throughout the data pipelines you build. Establish best practices for data quality, focusing on metadata management, documentation, and lineage tracking using established enterprise toolsets. Ensure that compliance risks and controls are identified; developed; documented; actioned; and monitored in line with the compliance obligations to mitigate risks of future compliance failure.
• Collaboration: Enable and productionize machine-learning features by working closely with data scientists and other stakeholders across the business.
• Continuous Improvement: Continuously monitor and improve data pipeline performance and reliability