Manager of Data Engineering

Manager Of Data Engineering (SGL
35)

Summit is a growing natural gas utility providing safe, reliable, and clean burning natural gas service to homes and businesses in Arkansas, Colorado, Maine, Missouri, Oklahoma, and Texas. Being part of the Summit team means embracing excellence and innovation, committing to safety each and every day, and doing all that we can to serve each other, our customers, and the communities where we live.

We aim to bring warmth and energy to everything we do.

We are pleased to announce an exciting opportunity for a Manager of Data Engineering (SGL
35) to join our team. This hybrid role may be based in one of our offices in Little Rock, Fort Smith, or Fayetteville, Arkansas.

A Manager of Data Engineering leads and mentors the data engineering team, overseeing the design, development, implementation, and maintenance of scalable and robust data infrastructure and pipelines. Successful candidates are strategic thinkers, possess strong leadership qualities, and are adept at managing complex data projects in a fast-paced environment. They are responsible for ensuring data quality, integrity, and accessibility across the enterprise.

A Manager of Data Engineering collaborates daily with IT leadership, data engineers, scientists, analysts, and business stakeholders to define data strategy, address data needs, and drive data-informed decision-making. They must foster a culture of innovation and continuous improvement within the data engineering team.

• Lead, manage, and mentor a team of data engineers and analysts, fostering their professional growth and development.
• Oversee the architecture, design, and implementation of enterprise-level data warehousing, data lakes, and data pipeline solutions.
• Define and enforce data engineering best practices, standards, and methodologies.
• Collaborate with cross-functional teams, including data science, business intelligence, and application development, to understand data requirements and deliver effective solutions.
• Ensure the reliability, scalability, and performance of data infrastructure and systems.
• Develop and implement strategies for data quality management, data governance, and data security.
• Manage the full lifecycle of data engineering projects, including planning, execution, monitoring, and delivery.
• Evaluate and recommend new technologies, tools, and techniques to enhance data engineering capabilities.
• Drive automation of data processes to improve efficiency and reduce manual intervention.
• Communicate effectively with technical teams and business stakeholders regarding project status, risks, and outcomes.
• Establish and monitor key performance indicators (KPIs) for the data engineering team and data systems.
• Troubleshoot and resolve complex data-related issues in a timely manner.
• Develop and manage the budget for the data engineering department.
• Stay current with industry trends and advancements in data engineering and big data technologies.
• Champion a data-driven culture within the organization.

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related quantitative field is preferred, or a combination of education and equivalent experience.
• 7+ years' experience in data engineering, with a proven track record of designing and implementing complex data solutions.
• 3+ years' experience in a leadership or managerial role, successfully leading and developing data engineering teams.

• Strong leadership, team-building, and interpersonal skills.
• Expertise in data modeling, ETL/ELT development, and data warehousing concepts (e.g., Kimball, Inmon).
• Proficiency in programming languages such as Python, Scala, or Java.
• Extensive experience with big data technologies (e.g., Apache Spark, Hadoop, Kafka, Flink).
• Deep understanding of cloud-based data platforms and services (e.g., AWS Redshift, S3, Glue; Azure Synapse, Data Lake Storage, Data Factory; Google Big Query, Cloud Storage, Dataflow).
• Proficient in SQL and experience with various database technologies (e.g., relational, No
  
  SQL, columnar).
• Experience with data pipeline orchestration tools (e.g., Apache Airflow, Prefect, Dagster).
• Solid understanding of data governance, data quality, data lineage, and data security principles and practices.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong project management skills, with the ability to manage multiple priorities and deadlines.
• Exceptional communication and presentation skills, with the ability to convey complex technical concepts to non-technical audiences.
• Experience with Dev Ops and Data Ops methodologies and tools (e.g., CI/CD, infrastructure-as-code).
• Familiarity with business intelligence tools (e.g., Power BI, Tableau) and their data integration needs.
• Strategic mindset with the ability to align data engineering initiatives with overall business objectives.

The above statements are intended to describe the…