Senior IT Data Engineer (Onsite)

Tyson Foods, Inc.•Springdale, AR

3d•Onsite

About The Position

The Senior IT Data Engineer is an expert in building and optimizing modern data platforms, including real-time streaming pipelines that are cost-optimized for cloud resources. This role leads complex projects across data engineering, enterprise data modeling, and agentic AI — architecting scalable solutions, enforcing governance and security, and deploying AI-powered autonomous workflows that transform data engineering practices. This position requires working side by side with users of the solution, understanding the opportunities, and rapidly iterating on the solution; architecting and building solutions that leverage business-critical data and the latest advancements in AI to solve them. You’ll work in small, agile teams and own the end-to-end execution and implementation of high-stakes projects for Tyson’s extensive manufacturing footprint. Very few companies provide the opportunity to work end-to-end projects and initiatives with such massive scale with significant could and data infrastructure already in place. With over 100+ manufacturing facilities worldwide, this position will be front and center influencing change and deploying technology to more than 100,000 team members.

Requirements

Bachelor's Degree or relevant experience.
3+ years of relevant and practical experience.
Proficiency in Python and SQL for data engineering at scale.
Expertise in modern data platforms (Databricks, Snowflake, BigQuery), lakehouse architectures (Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).
Deep knowledge of GCP.
Hands-on experience with orchestration (Airflow, Dagster), transformation (dbt), containerization (Docker, K8s), and IaC (Terraform).
Advanced data modeling, warehousing, dimensional modeling, and data contracts.
Expertise in CI/CD, data observability, governance, and cataloging.
Advanced expertise in agentic AI architectures, multi-agent systems, LLMOps, RAG pipelines, and AI safety/guardrails.
A highly analytical approach and eagerness to solve technical problems with data structures, storage systems, cloud infrastructure, front-end frameworks, and other technical tools.
Experience or curiosity about working with and using large-scale data to take on valuable business problems.
Ability to collaborate efficiently in teams of technical and non-technical individuals, and comfortable working in a dynamic environment with evolving objectives and iteration with users.

Nice To Haves

AWS Solutions Architect Professional, Google Professional Data Engineer, Databricks Certified Data Engineer Professional, or equivalent.
Project Management: Leading complex data and AI initiatives end-to-end.
Mentorship: Guiding team members in technical and professional growth.
Strategic Thinking: Aligning data and AI solutions with organizational goals.
Communication: Articulating strategies to technical and non-technical stakeholders.
Problem-Solving: Resolving complex data and AI system challenges.
Adaptability: Staying current with rapidly evolving technologies and practices.
Creativity: Innovating approaches to pipeline design, modeling, and AI automation.
Customer obsession
Team work & Collaboration

Responsibilities

Lead the design and orchestration of complex data pipelines and ETL/ELT processes using Python, SQL, and modern frameworks (e.g., dbt, Airflow, Dagster) for a $50B company.
Architect scalable data solutions using modern platforms (BigQuery), lakehouse patterns (Delta Lake, Iceberg), and event-driven streaming architectures (Kafka, Flink, Pub/Sub).
Must be able to design enterprise-wide data models using advanced techniques — dimensional modeling, multi-dimensional modeling, ERDs — ensuring consistency and alignment with business processes.
Define and implement data contracts and APIs to ensure reliable interfaces between data producers and consumers.
Establish and enforce data governance, security, cataloging, and stewardship standards across all data and AI systems.
Optimize cloud costs (AWS, GCP, or Azure) through efficient architecture and resource management.
Implement CI/CD pipelines for data workflows and manage containerized workloads (Docker, Kubernetes) with infrastructure as code (Terraform).
Must be able to work with DBT to model relevant data sources and ensure quality and uptime of that data
Drive data observability, including proactive monitoring, alerting, and automated detection of freshness, volume, and schema drift issues.
Lead code reviews, design and deploy agentic AI architectures and multi-agent systems that automate data engineering workflows, including RAG systems, vector databases, and LLM-integrated platforms.
Implement AI guardrails, observability, and evaluation frameworks, including LLMOps practices (prompt versioning, A/B testing, drift monitoring), cost optimization (token strategies, model selection), and security measures (prompt injection prevention, PII handling).
Lead code reviews, establish coding standards, perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.
Collaborating with fellow engineers on architecture and design decisions.
Must be able to work with the other developers on the team, specifically the data scientist and AI engineers to assist with what they need.
Wrangling massive-scale data and using AI to accelerate and enhance critical operations.
Developing custom applications tailored to customer needs.
Engaging directly with customer stakeholders, from consumers to technical teams and executives.