Data Engineer I (Onsite)

Tyson Foods, Inc.•Springdale, AR

2d•Onsite

About The Position

The IT Data Engineer I builds foundational skills in SQL, Python, and data pipeline development. Working under direct supervision, this role supports data ingestion, transformation, and quality processes while developing awareness of cloud platforms, data governance, and AI-assisted development tools. The position performs low-complexity data engineering tasks as part of a team, receiving instructions and guidance from others. It develops and maintains SQL-based data pipelines and Python scripts for transformation, ingestion, and automation; works with common data formats; and learns foundational data warehousing, architecture, and modeling concepts. The role also assists with data classification, governance, quality, privacy, testing, logging, and monitoring, escalating potential data exposure or classification issues to senior engineers. The engineer uses version control for code and configuration changes, gains exposure to cloud platforms and containerization, and collaborates with team members and stakeholders to understand data solution requirements. The role also develops awareness of AI/ML, prompt engineering, and AI-assisted tools, while validating AI-generated code, queries, and results before use.

Requirements

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.
Bachelor's degree or relevant experience.
Less than one year of relevant or practical experience.
Curiosity and eagerness to explore new data concepts, AI technologies, and emerging patterns.
Willingness to accept and act on coaching from senior engineers.
Attention to detail in data handling and analysis.
Ability to collaborate effectively in cross-functional teams and communicate technical concepts clearly and concisely.
Reliability in meeting deadlines and following through on commitments.
Willingness to learn new technologies, including AI-driven tools, and adapt to change.
Willingness and ability to perform the physical requirements of the job with or without reasonable accommodation.

Nice To Haves

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

Responsibilities

Deliver low-complexity data engineering tasks as part of a team under direct supervision.
Develop and maintain SQL-based data pipelines and 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.
Learn and apply foundational concepts of data warehousing, data architecture, and basic data modeling.
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 for code and configuration changes, including branching, committing, and participating in pull requests.
Gain exposure to major cloud platforms and understand how data services operate in cloud environments.
Develop basic awareness of containerization and how pipelines are packaged and deployed.
Learn data testing and validation practices using data quality frameworks.
Assist with basic logging and monitoring for data pipelines to support troubleshooting and operational visibility.
Collaborate with team members and stakeholders to understand data solution requirements.
Develop foundational understanding of AI/ML concepts and their application to data pipelines and automation.
Learn to use AI-assisted tools and copilots for code generation, debugging, and documentation.
Gain exposure to prompt engineering and how large language models can support data operations.
Validate and verify AI-produced code, queries, and results before use.