Lead IT Data Engineer -Onsite

Tyson Foods•Springdale, AR

3d•Onsite

About The Position

Tyson Foods is seeking a Lead IT Data Engineer to support the Beef & Pork Analytics team. This role will lead the design, development, governance, and operational support of enterprise data solutions that enable Fresh Meats reporting, analytics, and decision-making. The Lead IT Data Engineer will work across business and technology teams to deliver trusted data products for critical analytics use cases, Fresh Meats data lake capabilities, Power BI reporting, and other Beef & Pork business priorities. This position is expected to be a hands-on technical leader who can translate complex business needs into scalable data architecture, build and support cloud-based data pipelines, strengthen data quality and observability, and help ensure analytics solutions are secure, governed, reusable, and production-ready. The role requires strong partnership with product managers, business SMEs, data stewards, data governance partners, engineers, analysts, support teams, and platform teams to deliver data solutions that are accurate, traceable, and aligned to enterprise standards.

Requirements

Bachelor’s Degree in Computer Science, Information Systems, Data Engineering, Analytics, or related field, or equivalent combination of education and relevant experience.
5+ years of relevant and practical experience in data engineering, cloud data platforms, enterprise analytics, data warehousing, business intelligence, or related technology delivery.
Strong SQL skills and experience developing analytical datasets for enterprise reporting, dashboards, semantic models, and downstream analytics consumption.
Hands-on experience with cloud data platforms, preferably GCP and BigQuery, or comparable cloud data technologies.
Experience designing and supporting multi-layer data architectures such as lake, hub, curated, analytics, semantic, dimensional, star-schema, or medallion-style models.
Experience with ETL/ELT development, orchestration, pipeline monitoring, job dependencies, failure handling, and production support.
Experience working with source-system data, preferably including ERP, mainframe, DB2, SAP, manufacturing, sales, finance, pricing, or commodity-related datasets.
Understanding of data governance, metadata, business terminology, lineage, stewardship workflows, data classification, and controlled promotion into production.
Experience implementing or supporting data security controls, including role-based, row-level, and column-level access patterns.
Experience with Git-based development, merge requests, code review, deployment discipline, testing evidence, and change approval documentation.
Experience with Kimball data warehouse methodology in a medallion raw-cleansed-curated data architecture.
Ability to lead technical design discussions, identify risks, challenge assumptions, and recommend scalable, supportable solutions.
Technical Leadership: Serves as a senior technical contributor who can lead complex engineering work while coaching others toward durable, governed solutions.
Business Partnership: Builds strong relationships with business SMEs, product managers, analysts, data stewards, and platform teams to deliver analytics that solve real business problems.
Execution Ownership: Drives work from discovery and design through development, validation, production deployment, support, and continuous improvement.
Communication: Explains complex data concepts, technical tradeoffs, risks, timelines, dependencies, and decisions to both technical and non-technical stakeholders.
Detail Orientation: Values accuracy, traceability, and documentation, especially for business-critical reporting, validation, issue resolution, and audit-sensitive processes.
Strategic Thinking: Aligns data architecture and engineering choices to business outcomes, reuse, governance, security, performance, and long-term maintainability.
Mentorship: Develops engineers and analysts by reinforcing SQL quality, modeling standards, documentation, testing, support readiness, and problem-solving discipline.
Change Management: Helps teams adopt new data patterns, cloud practices, governance expectations, and proactive production support behaviors.

Nice To Haves

Google Cloud, data engineering, analytics, data governance, Power BI, or other relevant IT certification preferred.
Experience with GCP services such as BigQuery, Cloud Storage, Dataproc, Pub/Sub, Composer/Airflow, Dataflow, or related tools.
Experience with dbt, Python, PySpark, Spark, Terraform, CI/CD, or other modern data engineering technologies.
Experience with Power BI, AtScale, semantic layer design, Fabric capacity awareness, or enterprise reporting consumption patterns.
Experience with Fivetran, HVR, CDC, data replication, freshness checks, or source-to-cloud ingestion monitoring.
Experience supporting USDA, commodity pricing, sales realization, profitability, finance, supply chain, order/invoice, customer, product, or manufacturing analytics.
Experience with data quality frameworks, automated reconciliation routines, observability tooling, or audit-ready validation practices.
Experience supporting AI, machine learning, automation, or advanced analytics use cases through trusted and well-modeled data assets.

Responsibilities

Lead the design and implementation of data engineering solutions for Beef & Pork Analytics, including Fresh Meats data lake, hub, analytics, semantic, and reporting-ready data layers.
Translate business requirements, report needs, KPIs, functional specifications, and validation criteria into scalable data models, pipelines, transformations, and analytics-ready datasets.
Develop, enhance, and support data products that enable Fresh Meats reporting use cases.
Build and orchestrate complex ETL/ELT pipelines using approved enterprise patterns, automation, dependency management, monitoring, alerting, and production support practices.
Support DB2, SAP, USDA, and other internal or external data-source integrations into Data@Tyson/GCP, including replication, freshness monitoring, reconciliation, and exception handling.
Design data solutions for reuse across analytics projects by applying shared dimensions, conformed business definitions, enterprise business terms, and scalable modeling practices.
Implement and validate data security requirements, including role-based access, row-level security, column-level security, ARS roles, data classification dependencies, and DSS/security review requirements when applicable.
Partner with Data Governance, Data Stewards, Product Owners, Data Modeling Coaches, business SMEs, and reporting teams to ensure data assets are documented, classified, stewarded, and aligned to approved business terminology.
Support Collibra-related workflows by ensuring tables, fields, lineage, classifications, and business terms are identified, reviewed, and maintained as part of the delivery process.
Lead data quality, reconciliation, and observability practices so issues are detected proactively and can be traced from source systems through GCP layers and reporting outputs.
Participate in and lead architecture reviews, data model reviews, GitLab merge request readiness, release planning, production promotion, and change approval activities.
Provide operational support for Beef & Pork Analytics products, including incident triage, service requests, data lake freshness issues, access/security questions, pipeline failures, and production reporting impacts.
Coordinate with business and IT stakeholders during issue resolution by communicating impact, status, root cause, remediation steps, and validation results in a clear and timely manner.
Mentor data engineers and analysts in SQL, GCP, dbt, orchestration, data modeling, testing, documentation, support practices, and enterprise governance expectations.
Manage technical relationships with internal platform teams, vendors, and third-party partners when tools, integrations, or platform capabilities are needed to meet business needs.
Perform other assigned job-related duties that align with the organization’s vision, mission, values, and scope of practice.