Data Engineer

SUMMARY

The Data Engineer at Hercules Industries is responsible for building, governing, and continuously improving the data foundation that powers decision-making across supply chain, operations, and commercial functions. This role serves as both a steward of data integrity and a builder of scalable data infrastructure, enabling accurate, connected, and actionable data across the organization.

$90,000 - $107,000 per year, DOE

Own the design, integrity, and performance of Hercules’ data ecosystem by establishing scalable architecture, governing data quality, and enabling accessible, reliable data that drives operational and strategic decision-making across the business.

Other duties may be assigned.

• Establish and enforce data governance standards across ERP, data warehouse, and connected systems
• Ensure accuracy, consistency, and reliability of core datasets (item master, lead times, safety stock, supplier and customer data)
• Identify and remediate data quality issues through structured audits and data initiatives
• Define ownership, workflows, and controls for maintaining high-quality master data
• Implement monitoring systems to proactively detect and resolve data anomalies

• Establish high trust in enterprise data
• Eliminate systemic data errors impacting operations
• Ensure consistent, governed data definitions

• Design and maintain scalable data architecture connecting ERP (Infor M3), data warehouse, CRM, and external systems
• Build and manage data pipelines (ETL/ELT) to ensure timely and accurate data availability
• Enable seamless data flow across systems to support real-time and near-real-time decisions
• Standardize data models to support analytics and operational processes

• Deliver reliable, scalable, and efficient data pipelines
• Reduce latency and manual intervention across systems
• Enable seamless system integration

• Ensure MRP and planning systems are supported by accurate and structured data
• Partner with supply chain and operations to optimize lead times, safety stock, and planning parameters
• Identify and resolve data issues driving inefficiencies in purchasing, production, and inventory
• Support the evolution of MRP into a proactive, optimized planning system

• Improve effectiveness of MRP and planning systems
• Reduce operational inefficiencies caused by poor data
• Enable proactive planning capabilities

• Build data models that make data accessible for analysts, data scientists, and business leaders
• Develop datasets that enable rapid analysis and insight generation
• Support dashboards, reporting tools, and self-service analytics capabilities
• Ensure key business metrics are consistently defined and trusted

• Improve accessibility and usability of enterprise data
• Enable faster, more informed decision-making
• Establish consistency in key business metrics

• Partner with Data Science to provide clean, structured datasets for modeling
• Translate business requirements into data architecture solutions
• Work with supply chain, operations, and finance leaders to prioritize data needs
• Serve as a bridge between technical systems and business application

• Build strong cross-functional relationships
• Align data solutions with business needs
• Influence adoption of data-driven practices

• Lead initiatives to modernize data infrastructure and capabilities
• Identify opportunities to automate manual processes and eliminate workarounds
• Drive transition toward a fully integrated, data-driven operating model
• Establish best practices for data engineering within the organization

• Drive measurable improvements in efficiency and data reliability
• Reduce reliance on manual processes and spreadsheets
• Contribute to long‑term digital transformation

• Data accuracy, integrity, and reliability across systems
• System integration and data pipeline performance
• MRP and…