Data Analysis Engineer

Salcomp Manufacturing USA•Arlington, TX

About The Position

The Data Analyst Engineer is responsible for collecting, analyzing, and interpreting manufacturing and quality data to support process performance, decision-making, and continuous improvement. This role ensures data integrity and traceability in alignment with ISO 9001 requirements, customer expectations, and Salcomp reporting standards. Must maintain confidentiality of all company and customer information.

Requirements

Preferred bachelor’s degree in engineering, Industrial Engineering, Data Analytics, Statistics, or related field (required).
Minimum Certified Data Engineer – Associate
3–5 years of experience in data analysis within a manufacturing or industrial environment.
Experience supporting ISO 9001 certified manufacturing industry.
Strong understanding of manufacturing KPIs and quality metrics.
Working knowledge of ISO 9001, especially Clauses 7.1.5, 8.5.1, and 9.1.
Proficiency in Excel, SQL, Power BI, or similar tools.
Knowledge of SPC, DOE, capability analysis, and statistical methods.
Strong problem-solving, communication, and presentation skills.
Ability to work in a fast-paced manufacturing environment.

Nice To Haves

Certified Data Engineer
Microsoft Power BI Data Analyst
Data analytics or Power BI tool certifications
Databases and SQL for Data Science

Responsibilities

Collect, validate, and analyze data from manufacturing processes, quality systems, and production equipment. Data sources like SAP, MES, Cloud base spreadsheets.
Design, develop, and execute SQL scripts, VB Scripts, and other technologies to extract, transform, and load (ETL) data from various sources, particularly user interface (UI) data.
Conduct thorough data analysis to identify trends, patterns, and anomalies.
Develop and maintain data visualizations (graphs, charts, dashboards) using tools like Excel, Power BI to communicate findings effectively.
Develop and maintain dashboards and reports for Quality and Engineering, such like yield, scrap, rework, SPC reports, Downtime, FPY, defect trends (Pareto charts), Customer Complaints, Traceability reports.
Identify process variability, trends, and root causes using statistical and analytical methods.
Support ISO 9001 Clause 9.1 (Monitoring, Measurement, Analysis, and Evaluation) by ensuring accurate, reliable performance data.
Ensure data used for decision-making is controlled, traceable, and retained per documented procedures.
Provide data analysis to support Management Review, internal audits, and external audits.
Support continuous improvement initiatives using PDCA methodologies.
Perform statistical analysis (SPC, capability studies, Pareto analysis, trend analysis).
Support root cause analysis for nonconformities and customer complaints with data-driven insights.
Participate in the full execution of data conversions, including mapping, extraction, transformation, and validation.
Tune and optimize database objects, queries, and scripts for speed and efficiency.
Design, implement, and maintain new and existing database objects (tables, indexes, constraints, stored procedures, user-defined functions).
Identify, analyze, and recommend improvements to existing data processes and procedures.
Design and maintain automated reports and dashboards using approved tools (e.g., Excel, SQL, Power BI).
Translate complex data into clear, actionable insights for operations, quality, and leadership teams.
Standardize reporting formats to ensure consistency and comparability across shifts and lines.
Ensure accuracy, consistency, and completeness of manufacturing and quality data.
Support data governance practices, including access control, version control, and change management.
Collaborate with IT, Engineering and Customer on data extraction, system integration, and automation.
Work closely with IT, Quality, Engineering and Customer.
Support customer data requests and performance reports as required by the Quality Manager.
Train stakeholders in data interpretation and dashboard usage.
Recommend corrective actions based on data trends and analysis.
Escalate data integrity or performance risks to management.
Propose standardization of manufacturing and quality metrics.