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# Manager of Data Engineering

- **Company:** Summit Utilities, Inc.
- **Location:** Fort Smith, AR, United States
- **Experience:** Experienced
- **Salary:** $98,566.0 - $160,169.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon S3, Big Data, Cloud Storage, Databases, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Security, Data Systems, Data Warehousing, DevOps, Data Flow Control, Apache Hadoop, IT Management, Python (Programming Language), NoSQL, Power BI, DataOps, Software Engineering, SQL Databases, Tableau (Software), Cloud Platform System, Apache Spark, Data Strategy, Data Lakes, Information Technology, Data Lineage, Apache Flink, Google Bigquery, Apache Kafka, Azure Synapse Analytics, Data Pipelines, Amazon Redshift, Programming Languages
- **Published:** July 23, 2026
- **Apply:** https://www.jofdav.com/jobs/58958632-manager-of-data-engineering

## About the Role

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.

KNOWLEDGE, SKILLS, ABILITIES
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 BigQuery, Cloud Storage, Dataflow).Proficient in SQL and experience with various database technologies (e.g., relational, NoSQL, 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 DevOps and DataOps 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 general nature and level of work being performed by employees assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and/or skills required of all personnel so classified.

## Description

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.


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