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About This Role
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.
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Compensation: $99,000 - $130,700
Salary Context
This $99K-$130K range is below the median for Data Engineer roles in our dataset (median: $151K across 28 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,653 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Murphy USA, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,825 based on 160 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($114K) sits 35% below the category median. Disclosed range: $99K to $130K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Murphy USA AI Hiring
Murphy USA has 2 open AI roles right now. They're hiring across MLOps Engineer, Data Engineer. Positions span El Dorado, AR, US, NJ, US. Compensation range: $93K - $130K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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