Data Analyst Intern (Summer 2026)

apexanalytix•Greensboro, NC

2h•Remote

About The Position

We are seeking a detail-oriented and technically minded Data Analyst Intern to join our team. In this role, you will work within our on-premise data environment, ensuring the accuracy and reliability of the data that powers our business decisions. A significant portion of this internship focuses on data quality assurance (QA). You will ensure that our data is accurate, consistent, and reliable by performing rigorous testing and validation.

Requirements

Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Information Systems, Mathematics, or a related field.
SQL (Essential): Strong proficiency in writing SQL queries. You must be able to write queries to test data (e.g., finding set differences, counting variances).
Linux / Command Line: Comfort working in a Linux environment. You should know basic shell commands (bash) for file management and log inspection.
Kubernetes: Basic conceptual understanding of containerization. Familiarity with kubectl is a plus.
Data Concepts: Understanding of Data Warehousing (Star Schema) and ETL processes.
Programming: Basic proficiency in Python (Pandas) for scripting or data automation.
QA Mindset: A natural skepticism of data. You should have the habit of asking, "Does this number actually make sense?"
Attention to Detail: The ability to spot small discrepancies in large datasets.
Technical Curiosity: A desire to understand the infrastructure (servers/containers) that supports the data.
Communication: Ability to clearly report bugs and explain "why" a data point looks wrong to stakeholders.

Nice To Haves

Kubernetes: Familiarity with kubectl is a plus.

Responsibilities

Data Quality Assurance (QA) & Testing
Source-to-Target Validation: Perform comparative analysis between source systems and our Data Warehouse to ensure data was extracted and transformed correctly.
Regression Testing: Validate data outputs after system updates or pipeline changes to ensure existing reports and dashboards remain accurate.
Data Integrity Checks: Write SQL scripts to proactively identify nulls, duplicates, or schema mismatches before they impact the business.
Bug Reporting: Document data anomalies clearly and track them to resolution, working closely with engineers to identify the root cause.
Analysis & Technical Operations
Data Analysis: Write complex SQL queries to extract and manipulate data for ad-hoc business requests.
Infrastructure Interaction: Use the Linux command line to navigate servers, execute validation scripts, and grep logs for errors.
Kubernetes Support: Assist in monitoring data applications running on Kubernetes; check pod status and retrieve logs (kubectl logs) to aid in debugging QA failures.
Documentation: Maintain the Data Catalog and Business Glossary, ensuring that metric definitions match the technical reality of the data.
Supplier N-Tier Mapping: Research and identify data sources to build supplier n-tier maps, discovering relationships between Tier 1 suppliers and their upstream sub-suppliers (Tier 2, Tier 3, etc.).