Data Technician

Phillips & Jordan, Inc.•Knoxville, TN

38d•Onsite

About The Position

The Data Technician supports the Operations and Project Controls teams by organizing, validating, and maintaining high quality data from active field projects. This position is office based and focuses on data accuracy, reporting, and internal system support. The role improves the reliability of production data, cost tracking, material information, and schedule reporting used by leadership, project managers, and clients.

Requirements

High school diploma required. Associate degree in Business, Data Analytics, Engineering Technology, Computer Science, or a related field preferred.
One to three years of experience in a data focused, analytical, or administrative support role.
Proficiency in Python with the ability to automate data tasks, clean datasets, and support reporting workflows.
Proficiency in SQL with the ability to write queries, joins, and basic data transformations.
Strong skills in Excel
Strong attention to detail and commitment to data accuracy.
Candidates that accept an offer of employment are required to undergo and successfully complete a pre-employment physical examination. This exam is essential to ensure that all potential employees meet the necessary health and fitness standards for the role. The examination includes a series of tests and evaluations conducted by a certified medical professional to assess overall physical health, identify any potential medical conditions that could interfere with job performance, and verify that the candidate is capable of safely performing the essential duties of the position.
Completion and clearance of this physical are mandatory steps in the hiring process.

Nice To Haves

Experience in the power, utility, or construction industry is preferred but not required.
Experience with Power BI or similar reporting software is helpful.
C# experience is a plus for internal application support and system integrations.
C# experience is a strong plus for internal tool development, API connections, and data processing tasks.

Responsibilities

Review daily production data for accuracy, completeness, and consistency across all systems.
Identify data gaps or discrepancies and coordinate corrections with field leadership and project managers.
Maintain consistent data standards across all systems by applying clear naming, formatting, and coding practices that support accurate reporting.
Monitor incoming data within internal software systems to ensure quality, alignment, and proper integration with reporting tools.
Perform routine internal audits of datasets and reporting outputs to confirm accuracy, compliance with standards, and long-term data integrity.
Produce daily, weekly, and monthly reports that summarize production, material status, job performance, and schedule progress.
Build and maintain reporting tools and automated outputs using Python or similar scripting tools.
Create queries in SQL to extract, validate, and transform data for dashboards and analytics.
Support development of standardized reporting templates and visual summaries used by Operations leadership.
Maintain clean, reliable datasets that feed internal software systems, Power BI, and internal reporting tools.
Prepare structured data packets for project managers, finance, and leadership upon request.
Support PO and CO documentation by providing accurate production data, cost information, and historical references.
Assist with reconciling internal data against client records to confirm alignment before billing or reporting cycles.
Assist with integrating data between internal software systems, Power BI, Excel models, and SQL based sources.
Troubleshoot reporting issues and help refine data workflows to reduce manual effort.
Provide Python support for automation and data cleanup tasks.
Recommend updates to templates, checklists, and data processes to improve clarity and reduce errors.
Participate in periodic audits of job files, production logs, and cost tracking sheets.
Support training efforts for new personnel on reporting expectations and data quality standards.