The posting
It's exciting to work for a company that makes the world measurably better.
We're committed to bringing safety, quality, and customer focus to the business of advanced ceramics manufacturing.
Job Title
Forward Deployed Data Engineer
Must be a U.S. Person as defined under ITAR (U.S. citizen, U.S. national, lawful permanent resident, or protected individual under 8 U.S.C. § 1324b(a)(3)).
Must be able to work onsite five days per week at our Golden, CO facility.
The Forward Deployed Data Engineer works to understand workflows, data sources, data meaning, and decision needs, then translate those needs into governed data products, reusable data models, analytics, and AI-enabled solutions. The Forward Deployed Data Engineer serves as a hands-on bridge between plant operations, business leadership, and IT, enabling plant-level digitalization and AI deployment while improving enterprise insight and preserving appropriate plant-level flexibility. This role is embedded within manufacturing operations and focused on shop-floor execution, data readiness, and enabling AI-driven improvements. The role supports manufacturing data strategy by aligning plant data, ETL/ELT, data hierarchy, metrics, and semantic definitions so plant teams and central leadership can make faster, trusted data driven decisions. This role reports to a leader in the engineering team and works closely with IT, including Data & Analytics, Manufacturing IT/OT, Enterprise Applications, Cybersecurity, and Architecture.
Roles and Responsibilities
Understand workflows, constraints, decision points, and data needs embedded with manufacturing sites, business units, and functional teams.
Identify high-value opportunities for data, analytics, AI, or workflow enablement by partnering with plant leaders, engineers, quality, supply chain, maintenance, finance, and business leaders.
Executes hands-on, plant-level initiatives focused on improving efficiency, reducing manual work, and enabling AI and automation use cases.
Focus on digitalization of shop-floor processes, including elimination of paper-based workflows and improving data capture completeness.
Assess manufacturing data alignment across SAP, QAD, Apriso, Ignition, InfinityQS, LIMS, CMMS, equipment data, spreadsheets, databases, and other sources at a plant by plant level.
Translate ambiguous business and manufacturing problems into practical data requirements, data products, analytics, applications, and implementation plans.
Define mappings, data definitions, transformation rules, business logic, data quality rules, and metric calculations for trusted manufacturing insights.
Help establish an aligned manufacturing data hierarchy across sites, equipment, work centers, operations, products, materials, orders, quality events, and maintenance events.
Develop and/or support Databricks-based data products, pipelines, notebooks, dashboards, models, and applications using approved architecture and governance patterns with emphasis on applications and solutions that directly improve plant operations.
Partner with IT Data & Analytics on ETL/ELT patterns using Databricks, Delta Lake, Unity Catalog, workflows, governed tables, semantic definitions, and reusable data assets.
Balance local plant flexibility with enterprise standardization by helping define what should be harmonized centrally and what plant variation should be preserved while avoiding disruption to plant operations and prioritizing practical outcomes.
Support project-based data collection, cleaning, and integration efforts at the plant level to enable broader digitalization initiatives.
Improve data capture, completeness, quality, and ownership where source data is inconsistent, manual, incomplete, or not decision-ready.
Create minimum viable data products with real users, then mature successful solutions into governed, supportable production patterns, including Databricks-hosted applications.
Partner with IT architecture, cybersecurity, enterprise applications, integration, infrastructure, and manufacturing IT/OT to meet standards for identity, access, lineage, logging, supportability, resiliency, and responsible AI usage.
Document lineage, transformation logic, business definitions, solution designs, runbooks, ownership models, and reusable patterns that can scale across plants and business units.
Coach plant engineers, analysts, and business users on data definitions, data quality, workflows, analytics adoption, and responsible AI-enabled capabilities.
Serve as a point of contact for feedback loop between the business and IT by identifying recurring plant needs, architecture gaps, and reusable platform improvements.
Job Requirements
Education
Bachelor’s degree in Engineering, Industrial Engineering, Manufacturing Systems, Data Analytics, Computer Science, Information Technology, or a related field required.
Master’s degree preferred.
Experience
5 or more years of progressive experience in data engineering, analytics engineering, manufacturing systems, industrial technology, enterprise analytics, operational excellence, or a related field.
3 or more years working directly in manufacturing, plant operations, or industrial environments required.
Hands-on experience supporting shop floor operations, production systems, or industrial workflows is required.
Experience translating operational workflows into practical data, analytics, dashboard, pipeline, or application solutions.
Experience with Databricks, Delta Lake, Lakehouse architecture, SQL, Python, PySpark, data modeling, ETL/ELT, or modern data engineering practices.
Preferred experience with manufacturing systems such as:
- ERP Systems – SAP and QAD
- MES systems – Apriso
- SCADA Systems – Ignition
- Quality Systems – InfinityQS
- Other LIMS, CMMS, historians, or equipment data sources
Experience across these systems is highly desirable and considered a strong plus.
Preferred experience across multi-site or global manufacturing environments
Influencing outcomes without direct authority.
Functional / Technical Knowledge, Skills & Abilities
Strong ability to bridge plant operations, business leadership, and IT by translating manufacturing problems into data, analytics, application, and architecture requirements.
Strong understanding of manufacturing performance concepts such as yield, scrap, rework, throughput, cycle time, downtime, quality events, maintenance events, OEE, inventory, and production scheduling.
Strong understanding of Lean Manufacturing principles and continuous improvement methodologies (e.g., Kaizen, waste reduction, process flow improvement).
Strong working knowledge of data modeling, transformation, quality, semantic layers, metric definitions, metadata, lineage, and data governance.
Working knowledge of Databricks capabilities, including Delta tables, notebooks, workflows/jobs, SQL, Unity Catalog, data lineage, and governed analytical access patterns.
Ability to write and review SQL and Python-based data transformation logic; PySpark experience preferred.
Ability to define practical data hierarchies and translation layers that support local operational needs while enabling enterprise reporting and leadership insight.
Ability to develop prototypes, MVPs, dashboards, data products, and Databricks-enabled applications that validate value quickly and improve iteratively.
Ability to partner effectively with IT teams on architecture, cybersecurity, integration, enterprise applications, infrastructure, support, and lifecycle expectations.
Strong communication and documentation skills, including data dictionaries, mapping documents, process flows, business logic definitions, architecture notes, testing evidence, and runbooks.
Ability to manage multiple initiatives, prioritize by business value, work in ambiguity, and travel frequently for plant-facing data alignment and enablement.
Preferred Certifications
Relevant Databricks certifications, including Data Engineer, Data Analyst, Machine Learning, or Lakehouse Fundamentals preferred.
Relevant Microsoft Azure, Power BI, data engineering, analytics, AI, or cloud certifications preferred.
Lean Six Sigma, operational excellence, manufacturing systems, ISA-95, APICS/ASCM, or related industrial operations certifications are a plus.
Role Focus and Success Measures
- This is a plant-facing, execution-oriented role focused on shop-floor impact.
- The FDDE is not primarily a reporting/dashboard role, but a hands-on engineer enabling digitalization, data readiness, and AI deployment.
- Success is measured by:Reduction of manual / paper processes Improved data availability and quality Acceleration of AI and automation initiatives Measurable improvements in plant efficiency and productivity
Target Hiring Range
Annual Salary: USD 130,000.00 - USD 170,000.00
Actual compensation is commensurate with experience, skills and education. CoorsTek strives to give all qualified applicants equal opportunity and to make selection decisions on job related factors. Do not provide any information on the application which will indicate your race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity, pregnancy, genetic information, veteran status, or any other status protected by law or regulation.
If you like working for a company that makes a real difference in the world, you'll enjoy your career with us!
