Senior Data Engineer

At Summit Utilities Inc, the Senior Data Engineer leads the design, implementation, and ongoing modernization of our enterprise data pipelines, establishing platform standards that span cloud and on‑prem environments. This hybrid role is based in Arkansas with options in Little Rock, Fort Smith, or Fayetteville, and centers on enabling reliable data for operations, analytics, and regulatory filings. You will collaborate with Data Architects, Analytics Engineers, and IT leadership to shape the data engineering roadmap and mentor a team of engineers.

Responsibilities

• Own the end-to-end architecture of enterprise-scale data pipelines across hybrid environments, defining storage schemas, partitioning approaches, and orchestration patterns.
• Define the data engineering strategy and technical direction for functional areas, lead platform modernization initiatives, and authorize production deployments.
• Drive Python and SQL development standards across the team, including advanced transformation logic and the creation of reusable libraries for ingestion and integration.
• Architect cloud-based data services (Azure, AWS, Microsoft Fabric) with infrastructure-as-code, monitoring, alerting, and governance frameworks.
• Define platform-wide standards for error handling, logging, alerting, data validation, and data lifecycle management across all enterprise data flows.
• Lead cross-system integrations with SAP, GIS, SCADA, AMI, and external vendor systems via APIs, secure file exchanges, and cloud-native connectors.
• Serve as a formal mentor for Data Engineers I–III through code reviews, design sessions, and technical career coaching; may direct the work of 1–2 contractors or junior staff.
• Partner with Data Architects, Analytics Engineers, and IT leadership on platform standards, tooling decisions, and the long-term data engineering roadmap.
• Evaluate and select technologies for ingestion, orchestration, observability, and CI/CD; drive adoption and rollout across the data engineering team.
• Support regulatory filings (PHMSA, FERC, state PUC) and pipeline integrity data management with a reliable, auditable data infrastructure.
• Investigate complex, system-wide performance and reliability issues; lead troubleshooting efforts and propose durable architectural solutions.

Requirements

• Bachelor’s degree in computer science, Information Systems, Engineering, or a related field preferred; equivalent combination of education and relevant experience will be considered.
• 8–12 years of experience in data engineering, software development, or enterprise data integration roles.
• Extensive hands-on experience designing, building, and operating cloud data pipelines at enterprise scale.
• Proven track record architecting scalable enterprise data solutions across Azure and/or AWS environments.
• Strong experience integrating data from relational systems, cloud-native services, SAP, GIS, SCADA, and AMI.
• Demonstrated leadership delivering cross-functional data engineering programs and establishing development standards.
• Hands-on experience with infrastructure-as-code, CI/CD, source control, and automated deployment workflows.
• Master’s degree in a quantitative or technical field is preferred.
• Advanced Python and SQL mastery, including complex transformation logic, performance tuning, and reusable framework development.
• Expert cloud architecture skills across Azure and/or AWS, including data services, networking, security, and cost optimization.
• Deep expertise in designing end-to-end pipeline architecture for enterprise scale, including storage model design, partitioning strategies, and orchestration patterns.
• Proven leadership in cross-system integration spanning SAP, GIS, SCADA, AMI, and customer data systems.
• Hands-on experience with infrastructure-as-code (Terraform, Bicep, or similar) and platform monitoring & governance frameworks.
• Strong understanding of CI/CD, source control, and automated testing for data pipelines and platform components.
• Demonstrated ability to architect resilient, scalable, and cost-efficient data solutions, including CU tuning in Microsoft Fabric and other cloud platforms.
• Skilled at translating regulatory and business requirements into durable, well-governed data infrastructure; experience supporting PHMSA, FERC, and state PUC filings is preferred.
• Proven mentor and peer leader with experience guiding Data Engineers I–III through code reviews, design sessions, and career development.
• Strong written and verbal communication skills with the ability to influence technical and business stakeholders, including VP/Director-level partners.
• Deep working knowledge of natural gas distribution and utility data systems (billing, AMI, SCADA, SAP, GIS) and pipeline integrity data management.
• Strong problem-solving and root cause analysis skills for complex, distributed enterprise systems.
• Friendly, solution-oriented team player aligned with Summit’s PEAKS values.

Technologies

• Python
• SQL
• Azure
• AWS
• Microsoft Fabric
• Terraform
• Bicep
• CI/CD
• SAP
• GIS
• SCADA
• AMI
• APIs

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Competitive pay

Position Summary

Summit Utilities is seeking a Senior Data Engineer to lead the design, implementation, and ongoing modernization of enterprise data pipelines and platform standards that support operational, analytical, and regulatory workloads across cloud and on-prem environments. This role assumes program-level ownership of end-to-end pipeline architecture, defines storage, partitioning, and orchestration strategies, and approves pipeline deployments to production. The Senior Data Engineer mentors Data Engineers I–III, partners with Data Architects and IT leadership on platform standards, evaluates technologies, and supports PHMSA, FERC, and state PUC filings with a reliable, well-governed data infrastructure. Ideal candidates have 8–12 years of experience with advanced Python and SQL, deep Azure/AWS cloud expertise, infrastructure-as-code, CI/CD, and a proven track record delivering scalable enterprise data solutions in regulated settings.