Data Quality Analyst

About Us:

Barry-Wehmiller is a diversified global supplier of engineering consulting and manufacturing technology for the packaging, corrugating, sheeting and paper-converting industries. By blending people-centric leadership with disciplined operational strategies and purpose-driven growth, Barry-Wehmiller has become a $3 billion organization with nearly 12,000 team members united by a common belief: to use the power of business to build a better world.

Job Description:

The Data Quality Analyst ensures accuracy, completeness, and reliability of Barry Wehmiller’s data estate. The Data Quality Analyst is responsible for modeling, analyzing, profiling and cleaning data, collaborating with other data roles, application and security teams on data collection, preparation, storage and ongoing quality management and security, as well as working with users to optimize data for consumption. The Data Quality Analyst establishes data quality standards/rules, monitors data quality over time, plays a crucial role supporting data-driven decision-making.

This role bridges the gap between business and IT ensuring good data discipline and will develop procedures to enhance the accuracy and integrity of our organization's data including structured (quantitative) data, unstructured (qualitative) data, machine data, meta data, and big data.

The ideal candidate possesses extensive knowledge of data analysis techniques and experience in a similar role.

• Profile and audit datasets using tools or code to analyze data and identify anomalies, inconsistencies, and errors.
• Evaluate, recommend and document data standards.
• Gather data from primary and secondary data sources to identify opportunities and interpret trends.
• Analyze and test datasets to determine overall data quality and integrity.
• Create specifications and code data quality rules to evaluate data in a format that can be reviewed by owners, stewards and other stakeholders.
• Design and implement data quality metrics, create reports, and provide insights into data quality trends to ensure data meets business requirements.
• Identify the root cause of data quality issues and review findings with leadership to inform business decisions and prioritize system needs.
• Highlight data quality issues and work with stakeholders on clean-up, mapping and transformation rules.
• Evaluate system performance and design, as well as its effect on data quality.
• Collaborate with stakeholders to ensure mechanisms are defined to prevent data quality issues at the source, when possible.
• Lead and perform data clean-up activities through close collaboration with the business.
• Lead and make system improvements in data collection, preparation, storage and security processes.
• Create and maintain technical documentation including data quality processes, standards, and manuals.
• Train users and project teams on best practices and standards.
• Keep abreast of best practices in data quality and analysis.

• Proficiency in programming languages, including Structured Query Language (SQL).
• Experience with data analysis tools (e.g. Python, R, Julia, Power BI, MS Excel).
• Knowledge of statistical methods and tests, including Data Analysis techniques including Regression Analysis, Monte Carlo simulations, Factor Analysis, Cohort Analysis, Cluster Analysis, Time Series Analysis, and Sentiment Analysis.
• Experience with statistical packages (e.g. MS Excel, SAS, and SPSS).
• Exceptional analytical skills.
• Advanced problem‑solving skills.
• Knowledge of best practices in data analysis.
• Excellent interpersonal and communication skills.

• Bachelor's degree in statistics, mathematics, computer science, information management, or similar.
• 5+ years of experience in data analysis and modeling.
• 5+ years of system testing and quality assurance experience.
• Relevant Microsoft or cloud certifications (e.g., AZ-204, AZ-900, DP-900), preferred.
• Experience in large enterprise environments supporting both professional developers and low‑code/no‑code solutions. Manufacturing experience a plus.
• Familiarity with data governance and management platforms (e.g.…