Data Engineer III

Tyson Foods, Inc.•Springdale, AR

1d•Onsite

About The Position

Tyson Foods is seeking a highly skilled and business-oriented Data Engineer III to join our Enterprise Asset Management (EAM) organization. This role is responsible for advancing enterprise asset data strategies that optimize the full lifecycle management of manufacturing equipment, facilities, and infrastructure from design and construction through commissioning, operation, maintenance, and decommissioning. The Data Engineer III will operate across three integrated functional domains: Finance & Accounting, Application Development & Configuration, and Master Data Governance. This position serves as a critical bridge between business operations, finance, engineering, maintenance, and IT teams to ensure enterprise asset data is accurate, actionable, governed, and aligned to strategic objectives. This role requires a professional who can successfully balance day-to-day functional responsibilities with long-term strategic improvement initiatives ensuring operational support needs are met while continuously modernizing processes, systems, data quality, and business capabilities. This role will leverage advanced capabilities within SAP S/4HANA RISE Private Cloud (FICO, PM, PS, MM, EHS with Fiori UI/UX), Google Cloud Platform (GCP) or equivalent cloud data environments, Collibra or equivalent data governance platforms, and visualization/analytics solutions including Power BI, Palantir, Analysis for Excel, or equivalent business intelligence and analytics tools as well as AI-enabled development tools utilizing market-leading LLM technologies or equivalent solutions. The ideal candidate combines strong technical data engineering skills with asset-intensive business process knowledge, financial acumen, ERP configuration experience, enterprise data governance expertise, and the ability to lead complex cross-functional initiatives that improve operational reliability, capital efficiency, and governance maturity.

Requirements

Bachelor’s degree in Engineering, Construction Management, Information Systems, Computer Science, Finance, Supply Chain, or a related field.
5+ years of experience spanning data engineering, ERP systems, asset management, finance systems, manufacturing analytics, and/or data governance.
Strong applied knowledge of fixed assets, CIP accounting, maintenance processes, reliability, and manufacturing operations in an asset-intensive environment is required along with hands-on experience in SAP S/4HANA (PM, PS, MM, FICO preferred with exposure to Fiori deployment).
Demonstrated ability to balance operational support with strategic improvement initiatives is essential.
Experience with data governance platforms such as Collibra (or equivalent), cloud/data engineering tools (SQL, Python, ETL/ELT, GCP or equivalent), and analytics platforms such as Power BI, Palantir, Tableau, or Analysis for Excel are expected.
Industry experience in manufacturing asset-heavy environments is preferred.
Candidate will need to provide resume to be considered for this position.

Nice To Haves

SAP S/4HANA (PM, PS, MM, FICO preferred with exposure to Fiori deployment)
Collibra (or equivalent)
cloud/data engineering tools (SQL, Python, ETL/ELT, GCP or equivalent)
analytics platforms such as Power BI, Palantir, Tableau, or Analysis for Excel
manufacturing asset-heavy environments

Responsibilities

Support enterprise asset accounting processes including Construction in Progress (CIP), fixed asset capitalization, Maintenance & Repair (M&R) posting accuracy, reconciliation, and lifecycle cost management across multiple facilities.
Partner with Finance, Controlling, Engineering, and Operations teams to improve cost transparency, depreciation drivers, asset utilization, and educate stakeholders on property governance and total cost of ownership (TCO).
Develop dashboards and analytics using Power BI, Palantir, or equivalent tools to evaluate M&R performance, MRO inventory vs. replacement asset value (RAV), and cost optimization opportunities, incorporating engineered life drivers such as utilization, operating conditions, criticality, complexity, and maintenance demand.
Develop and maintain 5-year M&E CAPEX forecasts based on standardized engineered life assumptions with calculated replacement asset value (RAV) supporting capital planning, prioritization, and procurement price negotiation strategies.
Support audits, litigation requests, acquisitions, divestitures, plant closures, and due diligence, while maintaining governed financial data lineage, ownership, and controls through Collibra or equivalent platforms.
Serve as a key partner in the design, enhancement, support, and configuration of SAP EAM-related processes within SAP S/4HANA, balancing production support needs with long-term roadmap execution.
Collaborate with IT and third parties to integrate SAP with connected systems including data lakes, maintenance platforms, procurement tools, governance platforms, and analytics technologies or equivalent solutions.
Design, build, and optimize data pipelines into GCP or equivalent cloud environments, ensuring trusted enterprise reporting, data availability, and scalable analytics capabilities.
Develop UI/UX tools, automation solutions, low-code applications, self-service workflows, AI-assisted capabilities, and predictive asset insights that simplify user interaction and improve capital planning decisions.
Define and maintain enterprise standards for asset master data, equipment hierarchies, functional locations, cost structures, metadata, naming conventions, and business glossary definitions.
Administer or partner with governance teams to maintain Collibra or equivalent platforms including catalogs, dictionaries, lineage, stewardship assignments, workflows, ownership models, and policy controls.
Lead cleansing, harmonization, enrichment, and standardization initiatives for legacy and active asset data while balancing daily support requirements with long-term governance maturity goals.
Build data quality scorecards, exception reporting, and analytics to identify duplication, hierarchy gaps, classification issues, and opportunities to improve decision-grade data.
Lead medium to large cross-functional initiatives with minimal supervision, effectively prioritizing resources between immediate business demands and strategic transformation opportunities.
Translate complex technical findings into clear business recommendations for leadership, finance, engineering, maintenance, and IT stakeholders.
Mentor junior analysts, engineers, and data stewards on best practices in data engineering, governance, analytics, and enterprise asset management processes.
Drive a culture of continuous improvement by staying current on emerging technologies including cloud platforms, ERP modernization, AI/LLM tools, and manufacturing digital trends.