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, this role completes low-complexity data engineering tasks and supports data ingestion, transformation, and quality processes while developing awareness of cloud platforms, data governance, and AI-assisted development tools. The role develops and maintains SQL-based data pipelines and writes Python scripts for data transformation, ingestion, and automation. It also supports data classification, governance, quality, and privacy activities under guidance, and escalates potential data exposure or classification issues to senior engineers. The position uses version control, learns cloud and containerization concepts, applies basic data testing and monitoring practices, and collaborates with team members and stakeholders to understand data solution requirements. The role builds foundational understanding of AI/ML, prompt engineering, and the use of AI-assisted tools and copilots for code generation, debugging, documentation, and data operations. AI-generated outputs must be validated and verified before use. Team members are expected to use approved AI tools responsibly, protect sensitive information, follow company policies, and apply professional judgment to AI-supported work. A bachelor's degree or relevant experience is required. Less than one year of relevant or practical experience is specified. The role is onsite and on the first shift. Immigration/work authorization support and relocation assistance are not available. The successful candidate 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.
Ability to perform the physical requirements of the job, with or without reasonable accommodation.
Curiosity and eagerness to explore new 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 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.

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 for ingestion and transformation.
Learn and apply foundational data warehousing, data architecture, and data modeling concepts, including fact/dimension tables and normalization.
Assist with applying data classification standards and identifying data sensitivity levels under guidance.
Support data governance, data quality, and data privacy activities, including PII handling protocols and data retention policies; escalate potential exposure or classification issues to senior engineers.
Use Git or other 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 a cloud environment.
Develop basic awareness of containerization and how pipelines are packaged and deployed.
Learn and apply basic data testing and validation practices to ensure pipeline output quality.
Assist with basic logging and monitoring for troubleshooting and operational visibility.
Collaborate with team members and stakeholders to understand requirements for data solutions.
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 LLMs for data operations.
Validate and verify AI-produced code, queries, and results before use, and safeguard sensitive information when using approved AI tools.