Manager of Data Engineering

Murphy USA is investing in our people, data platform, and engineering practices to drive measurable business impact. We are looking for a Data Engineering Manager to lead the team responsible for building and operating trusted, secure, scalable data products and platform capabilities that support analytics and decision-making across the enterprise. 

GENERAL DESCRIPTION OF POSITION 

The Data Engineering Manager will lead the design, delivery, and ongoing operation of modern cloud-based data solutions. This role combines people leadership, hands-on engineering, technical direction, delivery accountability, and business partnership. The manager is expected to operate as a player-coach, balancing team leadership with direct engineering contributions to the design, development, and delivery of critical data products and platform capabilities. The successful individual will translate enterprise priorities into an actionable engineering roadmap, develop a high-performing team, establish standards for governed data delivery, and ensure data products and platform services are reliable, secure, reusable, and aligned with business needs. 

NOTE: This role is based in El Dorado, AR. 

ESSENTIAL DUTIES AND RESPONSIBILITIES 

• Lead, coach, and develop a team of data engineers by setting clear expectations, providing regular feedback, supporting career growth, and building succession and capability plans. 

• Operate as a player-coach by directly designing and building priority pipelines, data products, integrations, code, and reusable platform capabilities while leading team delivery. 

• Manage team capacity, vendor and contractor performance, technology investments, service-level commitments, operational priorities, and applicable budgets to ensure sustainable delivery and effective use of resources. 

• Attract, select, onboard, and retain strong engineering talent while fostering an inclusive culture of accountability, collaboration, learning, and ownership. 

• Provide technical direction for solution architecture and implementation across enterprise data products, batch and streaming pipelines, integrations, data modeling, and supporting platform capabilities. 

• Own the data engineering delivery roadmap, translating business priorities into sequenced work and managing scope, dependencies, capacity, timelines, risks, and outcomes. 

• Establish and reinforce engineering standards for code quality, testing, documentation, source control, automated deployment, validation, observability, performance, security, reliability, scalability, and reuse. 

• Own engineering operations by ensuring data products and platform services meet established service-level objectives through proactive monitoring, operational readiness, incident management, root cause analysis, preventive maintenance, and continuous improvement. 

• Drive practical data governance by embedding data quality rules, metadata management, lineage, access controls, ownership, and data protection requirements into engineering processes and data products. 

• Guide the effective use and evolution of technologies such as Databricks, Azure cloud services, Python, SQL, Infrastructure as Code, orchestration tools, and DevOps capabilities. 

• Influence enterprise data priorities and drive alignment across business units, analytics, data science, architecture, security, infrastructure, and peer technology teams to deliver strategic outcomes and resolve cross-functional dependencies. 

• Communicate delivery status, priorities, tradeoffs, risks, and decisions clearly to technical and non-technical stakeholders, resolving conflicts and building alignment across a matrixed organization. 

• Promote iterative delivery and continuous improvement, with emphasis on reducing time to insight, strengthening data trust, controlling cost, and improving the developer and data consumer experience. 

• Understand and provide recommendations that influence Murphy USA’s Data Strategy. 

• Lead modernization of enterprise data capabilities through cloud adoption, platform standardization, automation, migration of on-premises workloads, and retirement of legacy solutions where appropriate. 

• Perform any other related duties as required or assigned. 

QUALIFICATIONS 

• Demonstrated experience leading data engineering teams and delivering enterprise data capabilities in a complex, matrixed organization. 

• Experience building and operating data pipelines, data products, data lakes or lakehouses, data warehouses, and cloud-based data platforms. 

• Hands-on proficiency with modern data engineering languages and tools, including Python, SQL, Databricks, Azure cloud services, orchestration frameworks, source control, CI/CD, and Infrastructure as Code. 

• Experience establishing engineering practices for automated testing, deployment, observability, incident management, data quality, metadata management, lineage, security, access control, and operational excellence. 

• Ability to guide architecture and technical decisions across batch and streaming integration patterns, data modeling, performance, reliability, and platform scalability. 

• Experience developing roadmaps, managing competing priorities, allocating team capacity, and delivering through employees, contractors, and vendors. 

• Demonstrated ability to attract, develop, coach, and retain talent while creating accountability for outcomes and behaviors. 

• Strong business acumen and the ability to connect technical investments to measurable value such as improved decision speed, reduced risk, greater reliability, cost efficiency, or revenue enablement. 

• Strong written and verbal communication skills, including the ability to explain complex technical topics in plain language and build trusted relationships across the organization. 

• Comfort operating through change, ambiguity, and evolving priorities while maintaining focus on delivery and team health. 

• Experience in retail, convenience, fuel, consumer packaged goods, or another high-volume transactional environment is preferred. 

• Experience supporting analytics, reporting, data science, and AI/ML workloads through scalable data platform capabilities is preferred. 

• Experience with big Data Platforms such as Databricks, Snowflake, Fabric is preferred but not required. 

EDUCATION AND EXPERIENCE 

Broad knowledge of data engineering, software development, database technologies, cloud platforms, architecture, data management, and analytics. Equivalent to a four-year college degree in Computer Science, Management Information Systems, Engineering, Statistics, Analytics, or a related field, plus 8 years of related experience and/or training, including 3 years of formal or equivalent team leadership experience; or an equivalent combination of education and experience. A master's degree is preferred but not required. 

SCOPE AND DECISION-MAKING 

This role is accountable for the performance and development of the Data Engineering team, direct contribution to priority engineering work, the quality and reliability of delivery, engineering operations, and the technical standards used within its scope. The manager recommends priorities and investment tradeoffs, assigns resources, approves solution approaches within established architecture and security guardrails, completes individual engineering deliverables, manages applicable vendor, service, and budget commitments, escalates material risks, and partners with leadership on decisions that affect enterprise data strategy, platform direction, and cross-functional commitments. 

$123,900 - $165,400