Overview:
As a Senior Manager of platform Engineering, you will be a key techno-functional leader responsible for overseeing PepsiCo’s platform engineering operations. You will drive a strong vision for platform engineering, ensuring platform stability, observability, and cost optimization. Leading a team of platform engineers, you will build tools for platform operations and data operations within the PepsiCo Data Lake, enabling analytics, visualization, machine learning, and product development across the company. Additionally, you will oversee the platform governance team, implementing frameworks to enforce best practices for large-scale, complex data applications in cloud environments. You will collaborate closely with process owners, product owners, and business users to ensure alignment with business objectives.

Responsibilities:

• Lead platform cost optimization initiatives to enhance efficiency and reduce expenditures.
• Manage and scale Azure Data Platforms to support new product launches and ensure platform stability.
• Build and oversee automation and monitoring frameworks that capture operational KPIs and performance metrics for data platforms.
• Implement best practices for system integration, security, performance tuning, and platform management.
• Drive increased adoption of data, data science, and business intelligence to empower business operations.
• Collaborate with internal clients (data science and product teams) to drive solutioning and proof-of-concept (PoC) initiatives.
• Advance the architectural maturity of the data platform by working with enterprise architects and strategic partners.
• Develop and optimize procedures for deploying data science models into production environments.
• Define and enforce SLAs for platform and operational processes.
• Support large-scale experimentation and model deployment by data scientists.
• Research state-of-the-art methodologies to enhance platform performance and capabilities.
• Document learnings, best practices, and knowledge transfer strategies.
• Develop reusable packages or libraries to streamline platform engineering efforts.

EDUCATION:

• Bachelor’s degree in Computer Science, Information Systems, or a related technical field. A Master’s degree is a plus.
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  Our Company will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the Fair Credit Reporting Act, and all other applicable laws, including but not limited to, San Francisco Police Code Sections 4901-4919, commonly referred to as the San Francisco Fair Chance Ordinance; and Chapter XVII, Article 9 of the Los Angeles Municipal Code, commonly referred to as the Fair Chance Initiative for Hiring Ordinance.
  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
  PepsiCo is an Equal Opportunity Employer: Female / Minority / Disability / Protected Veteran / Sexual Orientation / Gender Identity.
  If you’d like more information about your EEO rights as an applicant under the law, please download the available EEO is the Law & EEO is the Law Supplement documents. View PepsiCo EEO Policy.
  Please view our Pay Transparency Statement

• Lead platform cost optimization initiatives to enhance efficiency and reduce expenditures.
• Manage and scale Azure Data Platforms to support new product launches and ensure platform stability.
• Build and oversee automation and monitoring frameworks that capture operational KPIs and performance metrics for data platforms.
• Implement best practices for system integration, security, performance tuning, and platform management.
• Drive increased adoption of data, data science, and business intelligence to empower business operations.
• Collaborate with internal clients (data science and product teams) to drive solutioning and proof-of-concept (PoC) initiatives.
• Advance the architectural maturity of the data platform by working with enterprise architects and strategic partners.
• Develop and optimize procedures for deploying data science models into production environments.
• Define and enforce SLAs for platform and operational processes.
• Support large-scale experimentation and model deployment by data scientists.
• Research state-of-the-art methodologies to enhance platform performance and capabilities.
• Document learnings, best practices, and knowledge transfer strategies.
• Develop reusable packages or libraries to streamline platform engineering efforts