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Data Engineer

Job in Little Rock, Pulaski County, Arkansas, 72208, USA
Listing for: DataJobs
Full Time position
Listed on 2026-09-29
Job specializations:
  • IT/Tech
    Data Engineering
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below

This entry-level Data Engineer I role supports the development, maintenance, and enhancement of enterprise data pipelines and integration solutions. The position focuses on building and validating reliable data workflows in a collaborative environment.

Role Overview

Summit Utilities Inc is hiring a Data Engineer I (SGL
17)
for a hybrid position in Little Rock, AR
. The role contributes to scalable data pipeline development and supports enterprise data integration through orchestration, scripting, and reliable SQL-based transformations.

Responsibilities
  • Build and maintain scalable, reliable data pipelines and integration workflows using modern orchestration tools, low-code platforms, and custom scripting across on-prem and cloud systems.
  • Ingest, transform, and stage structured and semi-structured data into enterprise data repositories, including writing, troubleshooting, and optimizing SQL queries, views, and stored procedures
    .
  • Support data quality and reliability by implementing validation checks, alerts, logging, and monitoring, and by partnering with Data Analysts and SAP Analysts to validate datasets and confirm accurate delivery.
  • Document data sources, pipeline logic, and transformations in shared repositories, and manage code through team-based source control and code review practices.
  • Participate in standups, sprint planning, and on-call rotations, responding to data support tickets and identifying process improvements and opportunities to adopt new technologies.
Required Qualifications
  • 1–3 years of experience in data engineering, ETL development, or data integration roles.
  • Bachelor’s degree in computer science, Information Systems, Engineering, or a related field preferred
    ; equivalent combinations of education and relevant experience will be considered.
  • Experience building or maintaining data pipelines using orchestration tools and/or custom scripting.
  • Practical experience with relational databases, including writing and tuning SQL
    , plus exposure to cloud-based data environments and collaborative source control.
  • Working knowledge of modern data orchestration and workflow automation tools, including building pipelines with both low-code platforms and custom code.
  • Solid understanding of relational databases with the ability to write and debug T-SQL or PL/SQL
    ; exposure to cloud data engineering concepts preferred.

    Understanding of data integration concepts, including file transfers (
    FTP/SFTP
    ), API-based ingestion, data staging, source control, and deployment automation.
  • Strong problem-solving and analytical skills with attention to data accuracy and reliability, plus the ability to document processes, logic flows, and system configurations clearly.
  • Effective verbal and written communication with analysts, engineers, and stakeholders; self-motivated, team-oriented, and aligned with PEAKS values and organizational goals.
Relevant Technologies
  • SQL, T-SQL, PL/SQL
  • ETL
  • FTP, SFTP
  • API-based ingestion
  • Data staging
  • SAP
  • Stored procedures, views
  • Source control, code review
  • Low-code platforms
Benefits
  • Competitive pay
  • Medical/dental/vision and other benefits
Education and Experience
  • Bachelor’s degree in computer science, Information Systems, Engineering, or a related field preferred; equivalent combination of education and relevant experience will be considered.
  • 1–3 years of experience in data engineering, ETL development, or data integration roles.
  • Experience building or maintaining data pipelines using orchestration tools and/or custom scripting.
  • Practical experience with relational databases, including writing and tuning SQL, plus exposure to cloud-based data environments and collaborative source control.
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