Data Engineer I (Onsite)

Tyson Foods•Springdale, AR

1d•Onsite

About The Position

The Data Engineer I builds foundational skills in SQL, Python, and data pipeline development. Working under direct supervision as part of a team, the role completes low-complexity data engineering tasks and supports data ingestion, transformation, data quality, and governance processes while developing awareness of cloud platforms and AI-assisted development tools. The role develops and maintains SQL-based pipelines and writes Python scripts for transformation, ingestion, and automation. It applies foundational concepts in data formats, warehousing, architecture, and modeling; assists with classification, privacy, and quality practices; and escalates potential data exposure or classification issues to senior engineers. It also uses version control, gains exposure to cloud platforms and containerization, and assists with testing, validation, logging, and monitoring. The Data Engineer I collaborates with team members and stakeholders to understand requirements. The position also develops awareness of AI/ML, prompt engineering, and AI copilots for engineering work, while learning to verify AI-generated code, queries, and results. Responsible use of approved AI tools includes protecting sensitive information, following company policies, and critically validating generated outputs. The role is onsite and listed for 1st shift. A bachelor's degree or relevant experience is required. Less than one year of relevant or practical experience is specified. The position is not eligible for immigration or work authorization support, or relocation assistance. Candidates must be willing and able to perform the physical requirements of the job, with or without reasonable accommodation.

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 explore data concepts, AI technologies, and emerging patterns.
Receptiveness to feedback and willingness to act on coaching from senior engineers.
Attention to detail in data handling and analysis.
Ability to collaborate effectively in cross-functional teams.
Clear and concise communication of technical concepts.
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.
Not eligible for 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

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, including JSON, CSV, Parquet, and Avro.
Apply foundational data warehousing, 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 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 and apply data testing and validation practices using data quality frameworks.
Assist with basic pipeline logging and monitoring for troubleshooting and operational visibility.
Collaborate with team members and stakeholders to understand requirements for data solutions.
Develop understanding of AI/ML concepts and their application to data pipelines and automation.
Use AI-assisted tools and copilots for tasks such as code generation, debugging, and documentation, and validate AI-produced code, queries, and results before use.
Use approved AI tools responsibly, safeguard sensitive information, follow company policies, and critically evaluate AI-generated outputs.