Principal Data Analytics - Maintenance & Reliability

BHP; Adelaide; On-site; AUD 180,000 - 260,000; Full time

Benefits offered by this job

Employee share plan

Performance-based bonuses

Flexible working options

Parental leave

Job summary

BHP's Data Analytics team seeks a Principal Data Analytics professional to guide maintenance analytics across the portfolio. You will design data models, govern pipelines, and translate insights into actionable recommendations for senior stakeholders.

You will lead cross-functional collaboration with data scientists, analysts, and subject-matter experts to drive reliability and cost efficiency in mining operations.

Qualifications

• Relevant tertiary qualification in Engineering, Science, IT or a related quantitative discipline.
• Strong SQL and Python skills with experience developing production-ready analytics solutions.
• Demonstrated experience designing data models, pipelines and schemas that other practitioners build on.
• Substantial reliability engineering or asset management experience in mining, manufacturing or similar heavy industry.
• Demonstrated experience in developing executive-level narratives and high-quality communication artefacts.
• Strong analytical, critical thinking and creative problem-solving skills.

Responsibilities

• Lead development of industry-leading maintenance analytics solutions.
• Own end-to-end data models and governed pipelines the team builds on.
• Quantify and assess the trade-off between reliability, cost and risk.
• Translate findings into artefacts and actions for senior leadership forums.

Skills

SQL

Python

Data modeling

Pipelines

Analytical thinking

Communication

Education

Tertiary qualification in Engineering/Science/IT

Job description

BHP's Data Analytics team seeks a Principal Data Analytics professional to guide maintenance analytics across the portfolio. You will design data models, govern pipelines, and translate insights into actionable recommendations for senior stakeholders.

You will lead cross-functional collaboration with data scientists, analysts, and subject-matter experts to drive reliability and cost efficiency in mining operations.