Job description

The Data & Feature Engineer is responsible for transforming enterprise data into production-ready assets that power Murphy USA's AI initiatives. This role serves as the foundation for machine learning models, AI applications, and intelligent decision-making systems by building high-quality data products, reusable features, and scalable pipelines. This position goes beyond traditional data engineering. Team members help solve complex business problems by partnering closely with AI engineers, business stakeholders, and technology teams to build in-house AI solutions tailored to Murphy USA's unique challenges. These solutions may support initiatives such as personalization, recommendation systems, forecasting, optimization, agentic AI, and generative AI applications. The ideal candidate is a self-starter who enjoys learning new technologies, taking ownership of outcomes, and continuously improving both technical capabilities and business understanding. While this role has a primary focus on data and feature engineering, team members are encouraged to broaden their skills across the AI lifecycle through cross-training and collaboration with AI/ML Engineers and AI/ML Operations Engineers. This position may be filled at Level I, II, or III based on the candidate's demonstrated capabilities, experience, and ability to contribute within the role. Essential Duties and Responsibilities • Build and maintain production-grade data pipelines using Python, PySpark, SQL, Databricks, and Azure. • Design, develop, and manage reusable feature stores that support machine learning and AI applications. • Transform raw enterprise data into reliable, scalable, and production-ready datasets. • Develop customer, transaction, store, operational, and behavioral features for AI and ML workloads. • Implement data validation, quality monitoring, lineage, and governance standards. • Partner with AI/ML Engineers to identify and develop features that improve model performance and business outcomes. • Create scalable data products that can be reused across multiple AI initiatives. • Support experimentation, exploratory analysis, and feature discovery activities. • Optimize data processing workflows for performance, reliability, cost, and maintainability. • Document data assets, transformations, dependencies, and engineering processes. • Collaborate with business stakeholders alongside the Manager of AI Implementation & Strategy to understand business challenges and identify opportunities to improve decision-making through data. • Contribute to engineering standards, reusable frameworks, source control practices, and knowledge sharing. • Assist with operational support, incident troubleshooting, and root-cause analysis for production data solutions. • Protect sensitive data and follow enterprise security, privacy, access-control, and data-management requirements. • Continuously expand technical capabilities through independent learning, experimentation, and cross-training opportunities. Preferred Qualifications • Bachelor's degree in Computer Science, Engineering, Mathematics, Information Systems, or a related discipline preferred, or equivalent practical experience. • Experience working with Databricks, PySpark, Python, SQL, and Azure. • Understanding of data modeling, ETL/ELT processes, data quality practices, and large-scale data processing. • Familiarity with machine learning workflows and feature engineering concepts. • Experience working with structured and unstructured data. • Experience with source control, testing, documentation, and collaborative software development practices is preferred. • Strong analytical and problem-solving abilities. • Ability to work independently, take initiative, and remain accountable for results. • Curiosity and willingness to learn new technologies, methodologies, and adjacent AI disciplines.

Requirements

Must-haves

Essential Duties and Responsibilities
Build and maintain production-grade data pipelines using Python, PySpark, SQL, Databricks, and Azure.
Design, develop, and manage reusable feature stores that support machine learning and AI applications.
Transform raw enterprise data into reliable, scalable, and production-ready datasets.
Develop customer, transaction, store, operational, and behavioral features for AI and ML workloads.
Implement data validation, quality monitoring, lineage, and governance standards.
Partner with AI/ML Engineers to identify and develop features that improve model performance and business outcomes.
Create scalable data products that can be reused across multiple AI initiatives.
Support experimentation, exploratory analysis, and feature discovery activities.
Optimize data processing workflows for performance, reliability, cost, and maintainability.
Document data assets, transformations, dependencies, and engineering processes.
Collaborate with business stakeholders alongside the Manager of AI Implementation & Strategy to understand business challenges and identify opportunities to improve decision-making through data.
Contribute to engineering standards, reusable frameworks, source control practices, and knowledge sharing.
Assist with operational support, incident troubleshooting, and root-cause analysis for production data solutions.
Protect sensitive data and follow enterprise security, privacy, access-control, and data-management requirements.

Nice-to-haves

Bachelor's degree in Computer Science, Engineering, Mathematics, Information Systems, or a related discipline preferred, or equivalent practical experience.
Experience working with Databricks, PySpark, Python, SQL, and Azure.
Understanding of data modeling, ETL/ELT processes, data quality practices, and large-scale data processing.
Familiarity with machine learning workflows and feature engineering concepts.
Experience working with structured and unstructured data.
Experience with source control, testing, documentation, and collaborative software development practices is preferred.
Strong analytical and problem-solving abilities.
Ability to work independently, take initiative, and remain accountable for results.
Curiosity and willingness to learn new technologies, methodologies, and adjacent AI disciplines.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonsqlazure

Platform: adp-recruiting
Effort: medium