Manufacturing Data Analyst – Automotive

generis tek•Georgetown, KY

4d•Onsite

About The Position

The Manufacturing Data Analyst applies manufacturing expertise, data analysis, and data modelling to help automotive organizations improve production performance, quality, cost, and efficiency. Working at the intersection of manufacturing operations, manufacturing systems, data engineering, and analytics, the role is responsible for understanding manufacturing data, developing data models, integrating information from multiple systems, and transforming complex operational data into actionable insights. The ideal candidate combines strong analytical and data-modelling capabilities with practical knowledge of automotive manufacturing environments, enabling them to understand not only what the data is showing, but also how manufacturing processes and systems should be represented within a data model.

Requirements

Experience in automotive manufacturing or a comparable discrete manufacturing environment.
Strong understanding of manufacturing processes and shop-floor operations.
Experience designing and developing data models for manufacturing analytics.
Strong SQL and data-querying skills.
Experience with relational data modelling, dimensional modelling, or semantic modelling.
Experience working with large and complex operational datasets.
Experience integrating data from multiple manufacturing and enterprise systems.
Experience with MES/MOM, ERP, QMS, PLM, SCADA, historians, or equipment data platforms.
Experience developing dashboards and analytics using Power BI, Tableau, or similar tools.
Strong understanding of data quality, data structures, relationships, and data governance.
Ability to translate manufacturing processes into logical data structures and analytical models.
Strong communication skills and the ability to work with both technical and manufacturing stakeholders.

Nice To Haves

Experience with Python, R, or other analytical programming languages.
Advanced SQL and data transformation experience.
Experience with Power BI semantic models, DAX, or similar analytical modelling technologies.
Experience with data warehouses, data lakes, lake houses, or modern cloud data platforms.
Knowledge of industrial data protocols and manufacturing technologies.
Experience with manufacturing information models or industrial data standards.
Experience with Lean Manufacturing, Six Sigma, or continuous-improvement methodologies.
Experience developing predictive or machine-learning models for manufacturing applications.
Experience connecting operational KPIs to financial and business outcomes.

Responsibilities

Analyze production, quality, equipment, material, labour, and operational data to identify trends, performance gaps, and improvement opportunities.
Identify patterns, anomalies, correlations, and relationships within manufacturing data.
Perform data-driven root-cause analysis.
Design and develop data models that represent manufacturing processes, equipment, materials, products, operations, quality information, and production events.
Develop logical and physical data models to support manufacturing analytics and reporting.
Define relationships between manufacturing entities such as plants, production lines, work centres, equipment, operations, products, materials, employees, and production events.
Identify gaps and inconsistencies between source-system data structures and the information required by manufacturing operations.
Work with business and technical stakeholders to validate data models against actual manufacturing processes.
Extract, transform, and integrate data from multiple manufacturing and enterprise systems.
Work with data from MES/MOM, ERP, PLM, QMS, WMS, SCADA, historians, equipment systems, and other operational data sources.
Map data elements between source systems and analytical data models.
Identify duplicate, missing, inconsistent, or poorly structured data.
Apply knowledge of automotive manufacturing processes to provide context to data analysis and modelling.
Understand production constraints, takt time, cycle time, line balancing, quality requirements, traceability, and production flow.
Validate data models and analytical results against actual shop-floor processes.
Develop dashboards, reports, and visualizations using tools such as Power BI, Tableau, or similar platforms.
Build operational reporting based on governed and reusable data models.