Manufacturing Data Analytics Jobs (NOW HIRING)

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Job Title
Forward Deployed Data Engineer
Forward Deployed Data Engineer works to understand workflows, data sources, data meaning, and decision needs, then translate those needs into governed Databricks data products, reusable data models, analytics, and AI-enabled solutions.
This role reports to a leader in engineering team and works closely with IT, including Data & Analytics, Manufacturing IT/OT, Enterprise Applications, Cybersecurity, and Architecture. The Forward Deployed Data Engineer bridges plant operations, business leadership, and IT to improve enterprise insight while preserving appropriate plant-level flexibility.
The role supports manufacturing data strategy by aligning plant data, ETL/ELT[RD1.1][DT1.2], data hierarchy, , metrics, and semantic definitions so plant teams and central leadership can make, faster, trusted data driven decisions.
Roles and Responsibilities

• Understand workflows, constraints, decision points, and data needs embed 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 .
• 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.
• 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 defining what should be harmonized centrally and what plant variation should be preserved.
• 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, Databricks 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.

• Bachelor's degree in Engineering, Industrial Engineering, Manufacturing Systems, Data Analytics, Computer Science, Information Technology, or a related field required.
• Master's degree preferred.

• 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 with manufacturing, plant operations, quality, supply chain, maintenance, engineering, or industrial data environments preferred.
• 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 SAP, QAD, MES, Apriso, Ignition, InfinityQS, LIMS, CMMS, SCADA, historians, or equipment data sources.
• Preferred experience across multi-site or global manufacturing environments and influencing outcomes without direct authority.

• 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 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.

• 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, or related industrial operations certifications are a plus.