Job Category:

Engineering

Job Family:

Operations Excellence

Job Description:

This Senior Operational Excellence Data Analyst is responsible for achieving operational and functional targets through independent ownership of moderately complex projects and assignments that directly impact team and job family results. Operating as an established professional under limited supervision, the individual contributes to defining project objectives and leads significant portions of initiatives that support operational performance. The role regularly communicates with partners within Operations and related functions on topics requiring explanation, interpretation, and advisement, influencing concepts, practices, and approaches at an operational level to enable effective execution and decision‑making.

The position focuses on identifying and implementing moderate improvements to processes, procedures, or systems that enhance team effectiveness and operational outcomes. Problems addressed are often difficult and mildly complex, with impact across multiple teams, requiring advanced job knowledge, sound judgment, and demonstrated functional competence. The role also provides assistance and training to partners to build capability, consistency, and adoption of best practices within the team and broader organization.

What you’ll do:

• Develop and Sustain SFI Data & Operational Excellence Culture
  Support, coach, and reinforce data-driven decision making and Operational Excellence principles with partners and Team Members. Champion measurement, structured problem-solving, and process discipline to drive positive partner behaviors and sustained performance improvement.

• Operational Data Enablement & Business Partnership
  Serve as the bridge between Operations and Schreiber’s technical data teams to translate complex data into actionable insights. Partner closely with operations, manufacturing engineering, process engineering, sourcing, packaging engineering, and FP&A to enable plant teams to identify and act on opportunities related to quality, delivery, capacity, and cost.

• Production Data Needs & Use-Case Definition
  Understand, define, and prioritize the data needs of production teams to support effective business decisions. Apply structured improvement methodologies (e.g., Lean Six Sigma) to support cost, quality, and operational performance initiatives through disciplined data use.

• Data Structure Development, Collection, and Preparation
  Develop, standardize, and maintain data structures and data pipelines that enable reliable data collection from multiple sources. Ensure data accuracy, completeness, and consistency to support trusted analytics and KPI reporting.

• Data Exploration, Analysis, and Model Development
  Apply analytical and statistical methods to explore data, identify trends, and uncover root causes. Where appropriate, develop and maintain models to forecast performance and support proactive, data-based decision making.

• Data Visualization, KPI Standards, and Reporting
  Establish standards and develop clear, intuitive data visualizations using tools such as Power BI. Translate insights into visual formats that are easily understood and actionable, including leadership dashboards and plant-floor visualizations (e.g., HMI screens), to drive alignment and execution.

• Training, Enablement, and Capability Building
  Provide targeted training, standards, and enablement that build Operations’ capability to independently access, analyze, and visualize data. Empower partners to adopt best practices in data analytics and visualization to sustain results beyond direct support.

• Cross-Functional Collaboration & Continuous Improvement Leadership
  Actively collaborate across functions to promote best practices in data access, analytics, and KPI visualization. Champion continuous improvement through effective use of data, analytics, and insight-driven problem resolution.

• May be required to perform other job-related tasks. 

What you need to succeed:

• Bachelor’s degree in Engineering, Data Analytics, Operations or related technical field.

• 7+ years experience in manufacturing/operations, engineering, technical or related area in building and interpreting operational metrics, dashboards, and analysis.

• Proficiency in Excel, SQL, Power BI, and/or Tableau

• Knowledge/proficiency of statistical programming languages like Phyton or commitment to develop proficiency within 12-24 months is expected.

• Data collection, cleaning and governance practices

• Lean and Six Sigma disciplines where app