Product & Program Definition Specialist
Chennai, Tamil Nadu, India
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Ford Motor Company
Since 1903, we have helped to build a better world for the people and communities that we serve. Welcome to Ford Motor Company.Be a key player in Ford's exciting journey to reshape its product and program data!
As a Program and Product Structure Transformation Specialist, you will contribute to a company-wide initiative to redefine our product and program data landscape. Working closely with the Transformation Manager and cross-functional teams, you will analyze existing data, develop and implement data transformation processes, and ensure data quality. This role offers a significant opportunity to leverage your data expertise and drive impactful change within a large-scale project.
- Analyze existing product and program data structures to understand their content, quality, and relationships.
- Decompose complex data structures into foundational elements (entities, attributes, relationships).
- Identify value-added vs. non-value-added data elements and propose strategies for data optimization
- Collaborate with cross-functional teams to understand the desired future state product and program data structures.
- Map current state data elements to future state elements, identifying gaps and potential transformation challenges.
- Develop and document data mapping specifications.
- Develop transformation logic and rules for converting data from the current state to the future state, ensuring data integrity and consistency.
- Create and maintain comprehensive documentation for data transformation processes.
- Develop, test, and execute data transformation scripts and processes.
- Validate and cleanse transformed data to ensure accuracy and completeness.
- Identify and troubleshoot data quality issues during the transformation process, implementing corrective actions.
- Work closely with cross-functional teams, including engineering, manufacturing, IT, and business stakeholders.
- Communicate effectively with team members and stakeholders to share progress, identify risks, and resolve issues.
- Participate in meetings and workshops to contribute to the overall transformation strategy and provide data-driven insights.
- 5-7 years of experience in data analysis, data management, or a related field, with a focus on data transformation.
- Strong understanding of data elements, data types, and data relationships.
- Excellent analytical and problem-solving skills, with the ability to analyze complex data sets and identify patterns.
- Ability to think logically and systematically about data transformation processes and develop efficient solutions.
- Bachelor's degree in a related field (e.g., Computer Science, Data Science, Engineering, Business).
- Excellent written and verbal communication skills, with the ability to present technical information to both technical and non-technical audiences.
- Ability to work effectively in a team environment and collaborate with diverse stakeholders.
- Proficiency in Microsoft Excel (including advanced data manipulation and analysis functions).
- Strong proficiency in data query languages (e.g., SQL).
- Experience with data modeling tools.
- Experience in an engineering or manufacturing environment is highly desirable.
- Familiarity with PLM systems and product development processes is a plus.
- Experience with Microsoft Teams, SharePoint, and OneNote.
Bonus Points:
- Experience with data transformation tools and ETL processes.
- Proficiency in programming languages (e.g., Python) for data analysis and automation.
- Experience with data visualization tools (e.g., PowerBI, Tableau).
- Knowledge of data quality principles and practices and experience implementing data quality initiatives.
- Experience with Agile development methodologies.
- Familiarity with Product & BOM (EBOM, MBOM & BOP).
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Agile Computer Science Data analysis Data management Data quality Data visualization Engineering ETL Excel Power BI Python SharePoint SQL Tableau
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