Senior Data Analyst

Sherwin-Williams•Minneapolis, MN

About The Position

This position is an excellent opportunity for an individual to contribute to the digital initiatives and data visualization projects of the Sherwin Williams General Industrial business unit. This position is accountable for the development of visualization and reporting solutions to support functional areas of R&D, analysis to support simplification, cost savings, efficiency, product analysis and Continuous Improvement initiatives. The successful candidate will be versed in data analytics and visualization solutions, well organized, capable of managing multiple project assignments, and effective in written and verbal communication, with an ability to convey complex and technical information to diverse audiences. Creativity and a proven ability to collaborate are also key for this role as this individual will manage relationships with stakeholders and become a subject matter in support of new and existing analytics providing accurate views of data along with insight in how it can be used to improve processes and products. Job duties include contact with other employees and access confidential and proprietary information and/or other items of value, and such access may be supervised or unsupervised. The Company therefore has determined that a review of criminal history is necessary to protect the business and its operations and reputation and is necessary to protect the safety of the Company’s staff, employees, and business relationships.

Requirements

Must be legally authorized to work in country of employment.
Master’s degree in a Science, Technology, Engineering or Math field.
Must be at least 18 years of age.
Minimum 3 years of experience working with data analysis.

Nice To Haves

Bachelor’s degree in Chemistry, Engineering
Strong background in Product Vision, Business Objects, Excel, Oracle (OBIEE) and Tableau.

Responsibilities

Lead data strategy within General Industrial R&D, integrating and refining complex, multi-source datasets (formulation, process, and operational) to ensure accuracy, consistency, and usability for decision-making.
Design and execute advanced data analyses and queries, establishing robust validation frameworks to improve data integrity and confidence across global datasets.
Translate business and technical requirements into scalable, high-impact visualizations, enabling clear linkage across processes, raw materials, formulations, and performance outcomes.
Drive global data standardization and alignment, partnering with regional GI teams to harmonize definitions, metrics, and reporting structures across USCA, LATAM, EU, and APAC.
Partner with enterprise data architects and system owners to influence data model design, governance practices, and system integration strategies that support R&D and quality excellence.
Identify systemic data quality gaps, implementing sustainable process improvements to prevent recurrence and improve downstream analytics. Including recommendations to modify processes and systems impacting input data as well as focus on the output data and visibility to aid in Quality program/proactive projects.
Collaborate with IT and digital teams to architect data pipelines and enable advanced analytics, ensuring accessibility of critical datasets within Power BI, Tableau, Snowflake, and other platforms.
Develop advanced data models and integrated datasets using tools such as Snowflake, Dataverse, Oracle, and Excel, enabling scalable and repeatable analysis across projects and regions.
Lead analytics targeted at process improvement initiatives, allowing Technical/Quality managers to using statistical insights and visualization to identify optimization opportunities in product performance, cost, and manufacturing robustness.
Manage and influence digital tool enhancements (e.g., Navigator, Product Vision) to improve usability, adoption, and alignment with R&D and quality workflows.
Serve as a global leader in data literacy, building capability through training, coaching, and strategic communication to shift the organization toward self-service, insight-driven decision making.
Translate complex data into actionable insights for senior leadership, highlighting risks, opportunities, and trade-offs with clarity and precision.
Proactively surface trends and signals across global datasets, anticipating risks and identifying opportunities for innovation, process optimization, and competitive advantage.