Senior IT Data Engineer; Onsite

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.

• 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 (Big Query), 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.

• Education:
  
  Bachelor's Degree or relevant experience.
• Preferred Certification(s): AWS Solutions Architect Professional, Google Professional Data Engineer, Databricks Certified Data Engineer Professional, or equivalent.
• 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, Big Query), lakehouse architectures (Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).
• Deep knowledge of GCP.
• Hands‑on experience with…