Specialist Data Engineering

Rio Tinto•Salt Lake City, UT

About The Position

We’re Finding Better Ways™ to provide materials the world needs, now and in the future. Our values – care, courage and curiosity – guide how we work and how we treat each other. About the role We are seeking a Data engineer to build and maintain data pipelines that collect, transform, and load data from plant control systems, mine planning databases, and other operational sources into governed data environments—enabling practical, data-driven decisions in mining/manufacturing operations. This includes building and operating pipelines in a Databricks lakehouse environment (e.g., Apache Spark, Delta Lake) to support scalable ingestion, transformation, and serving patterns. This role focuses on the delivery of high-quality, timely data to operations and management, supporting safety, productivity, and cost efficiency. The data engineer collaborates with site teams, analysts, and business stakeholders to understand data needs, troubleshoot issues, and deliver reliable reporting and analytics. Your work enables product teams to deliver value at Kennecott, Oyu Tolgoi, Winu and Resolution Experience with operational technology (OT) data, plant systems, sensors, and historian platforms (e.g., PI, SCADA, ODP (Operational Data Platform)) is beneficial, with use cases including operational reporting, asset health monitoring, and process optimization. Databricks experience should include strong data governance practices (e.g., Unity Catalog permissions, lineage, and secure access patterns) and operational ownership of jobs/workflows.

Requirements

Data Engineering: build, run, and improve reliable data pipelines (batch and near-real-time) and pipeline orchestration.
Problem solving and data quality: diagnose issues, implement monitoring, and apply pragmatic controls to improve data quality and reliability.
Stakeholder Relationship Management: work closely with product managers, digital teams, and site operations to understand data needs and deliver practical solutions.
Information Security : design and operate solutions that align with governance, security, and compliance expectations in operational (mining/manufacturing) environments.
Databricks proficiency: develop and productionize notebooks and Spark code, manage Delta Lake tables, and use repos/CI-CD practices to promote changes safely.
SQL and Python for data transformation, testing, and automation.
Experience using Databricks notebooks and Spark APIs (PySpark/SQL) to implement transformations at scale.
Cloud data platforms (Azure and/or AWS) and modern data tooling; ability to operate solutions with performance and cost awareness.
Databricks lakehouse fundamentals including Delta Lake (ACID, schema evolution), table design, and medallion (bronze/silver/gold) patterns.
ETL (Extract, Transform, Load), data modeling, and pipeline orchestration.
Databricks Jobs/Workflows orchestration and CI/CD practices (e.g., using repos, code reviews, and automated testing) to deploy changes safely.

Nice To Haves

Operational technology (OT) data and historian/plant systems (e.g., PI, SCADA, sensors), and experience in mining, manufacturing, or heavy industry (preferred).
Familiarity with secure data sharing and access controls in Databricks (e.g., Unity Catalog) is a plus.

Responsibilities

Design, build and maintain scalable data pipelines across operational and enterprise systems with a strong focus on data pipeline management and operational reliability.
Build and operate data pipelines using Databricks (Spark) with Delta Lake tables and standard lakehouse patterns (e.g., bronze/silver/gold) to support analytics and reporting.
Implement and support Databricks Jobs/Workflows, monitoring, and performance/cost tuning (e.g., cluster configuration, Spark optimization) to ensure reliable operations.
Use Databricks Unity Catalog (or equivalent) to manage data access controls, cataloging, and lineage in line with governance, security, and compliance standards.
Integrate data from OT, IT, vendor and cloud platforms into governed data environments.
Ensure data quality, reliability, and timeliness for analytics and product use cases by implementing data quality assurance checks, monitoring, and clear data issue management.
Apply Rio Tinto’s data governance, security, and compliance standards when designing, building, and operating data solutions.
Troubleshoot and resolve data issues to ensure reliable reporting and analytics for business users.
Identify opportunities for automation and process improvement to increase productivity and reduce manual work.
Support continuous improvement by sharing best practices and contributing to the data engineering community.
Collaborate with product managers, data scientists and analysts to support product development.
Implement data models optimised for analytics, reporting and machine learning.
Monitor and improve performance, cost and reliability of data platforms.
Contribute to documentation, standards, and reusable data engineering patterns.
Support production issues and continuous improvement of live data products.

Benefits

Competitive base salary reflective of your skills and experience with an annual incentive bonus
Access to top-tier family-friendly health programs including medical, prescription drug, dental, vision, life insurance coverage, and various voluntary benefits
Excellent retirement plan including up to 6% defined company contribution
Generous 401k matching program
Attractive share ownership plan
Comprehensive leave policies which cover all the moments that matter in life: PTO (paid time off), paid holidays, and paid bereavement leave
A generous parental leave policy, which ensures you’re able to spend quality time with your new family
Discount programs for leisure purposes and access to Personify Health with incentives supporting your wellbeing
Access to our Employee Assistance Program (EAP), which provides confidential counselling or expert guidance for you and your family to help get through life’s challenges

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