Data Engineer I (Onsite)

Tyson Shared Services•Springdale, AR

7d•Onsite

About The Position

The IT Data Engineer I builds foundational skills in SQL, Python, and data pipeline development. Working under direct supervision and as part of a team, this role completes low-complexity data engineering tasks and supports data ingestion, transformation, data quality, governance, and privacy processes. The position develops awareness of cloud platforms, data architecture, data governance, and AI-assisted development tools. The Data Engineer I develops and maintains SQL-based pipelines and writes Python scripts for data transformation, ingestion, and automation. The role learns to work with common data formats, basic data warehousing and modeling concepts, data classification standards, version control, cloud data services, containerization, testing, logging, and monitoring. It also collaborates with team members and stakeholders to understand data solution requirements. The role develops foundational AI/ML knowledge and learns to use AI-assisted tools for engineering tasks such as code generation, debugging, and documentation. AI-generated code, queries, and results must be validated before use. The successful candidate must be willing and able to perform the physical requirements of the job, with or without reasonable accommodation. The position is onsite. Immigration or work authorization support and relocation assistance are not available.

Requirements

Bachelor's degree or relevant experience.
Less than one year of relevant or practical experience.
Proficiency in SQL and foundational Python scripting.
Understanding of data warehousing concepts, basic data modeling, and basic data architecture.
Familiarity with common data serialization and interchange formats.
Foundational exposure to modern cloud data platforms and at least one major cloud platform, such as AWS, GCP, or Azure.
Basic proficiency with version control systems for collaborative development.
Basic awareness of AI/ML concepts and AI-assisted development tools for code generation and data analysis.
Curiosity and eagerness to learn about data concepts, AI technologies, and emerging patterns.
Willingness to accept and act on coaching from senior engineers.
Attention to detail when handling and analyzing data.
Ability to collaborate effectively in cross-functional teams and communicate technical concepts clearly and concisely.
Reliability in meeting deadlines and following through on commitments.
Adaptability and willingness to learn new technologies, including AI-driven tools.
Willingness and ability to perform the physical requirements of the job, with or without reasonable accommodation.
Must not require immigration or work authorization support now or in the future.

Nice To Haves

Any relevant IT certification, such as AWS Cloud Practitioner, Google Cloud Digital Leader, Azure Fundamentals, or Databricks Lakehouse Fundamentals.

Responsibilities

Complete low-complexity data engineering tasks as part of a team under direct supervision.
Develop and maintain SQL-based data pipelines and support efficient ETL processes.
Write Python scripts for data transformation, ingestion, and automation.
Work with common data serialization and interchange formats such as JSON, CSV, Parquet, and Avro for ingestion and transformation.
Learn and apply foundational data warehousing, data architecture, and data modeling concepts.
Assist with applying data classification standards and identifying data sensitivity levels under guidance.
Support data governance, data quality, and data privacy activities, including following PII handling protocols and data retention policies.
Escalate potential data exposure or classification issues to senior engineers.
Use version control systems such as Git for code and configuration changes, including branching, commits, and pull requests.
Gain exposure to cloud platforms and understand how data services operate in a cloud environment.
Develop awareness of containerization and how pipelines are packaged and deployed.
Learn basic data testing and validation practices using data quality frameworks.
Assist with basic pipeline logging and monitoring to support troubleshooting and operational visibility.
Collaborate with team members and stakeholders to understand requirements for data solutions.
Learn to use AI-assisted tools and copilots for code generation, debugging, documentation, and other data engineering tasks.
Validate and verify AI-produced code, queries, and results before use.
Safeguard sensitive information and follow company policies when using approved AI tools.