Job Description:
Are you passionate about Data and Analytics (D&A) and excited about how it can completely transform the way an enterprise works? Do you have the strategic vision, technical expertise, and leadership skills to drive data-driven solutions? Do you want to work in a dynamic, fast-growing category? If so, you might be the ideal candidate for the role in the Data and Analytics function for Global Pet Nutrition (PN) at Mars.
Pet Nutrition (PN) is the most vibrant category in the FMCG sector. As we work to transform this exciting category, a new program, Digital First, has been mobilized by the Mars Pet Nutrition (PN) leadership team. Digital First places pet parents at the center of all we do in Mars PN, while digitalizing a wide range of business process areas, and creating future fit capabilities to achieve ambitious targets in top line growth, earnings, and pet parent centricity. The Digital First agenda requires Digitizing at scale and requires you to demonstrate significant thought leadership, quality decision making, deep technical know-how, and an ability to navigate complex business challenges while building and leading a team of world class data and analytics leaders.
With Digital First, PN is moving to a Product based model to create business facing digital capabilities. Develop and maintain robust data pipelines and storage solutions to support data analytics and machine learning initiatives. Reporting to the Director-Data engineering solution, The role operates globally in collaboration with teams engineering teams across growth products.
Technical Leadership - Provide strong technical leadership to data engineers and DevOps engineers across growth product teams. Act as a thought partner in the design, implementation, and evolution of scalable data platforms and assets. Champion best practices in data engineering and foster a collaborative, innovative, and high-performance culture across teams.
Engineering Standards and Frameworks: Define, maintain, and evolve data engineering standards, patterns, and frameworks that product teams can adopt. Ensure consistency, quality, and reusability across solutions. Serve as a point of accountability for technical decisions and architectural direction, while empowering product teams to execute effectively.
DataOps Enablement and Optimization: Drive the adoption of modern DataOps principles to streamline engineering workflows. Partner with platform teams to establish CI/CD pipelines, observability standards that improve operational efficiency, reliability, and speed across data pipelines.
Data Governance and Quality Assurance: Embed governance, security, and data quality practices into engineering workflows. Define guardrails and reference implementations for data access control, data lineage, and compliance. Promote consistent metadata management and enforce technical standards to ensure trust in data assets.
Stakeholder Engagement: Collaborate with PN D&A leadership, PN product owners, and segment D&A leadership to synchronize and formulate data priorities aimed at maximizing value through data utilization.
Data Engineering Leadership: Ability to lead global technical teams in building scalable data platforms, data pipelines, and infrastructure that supports advanced analytics and machine learning.
DataOps & DevOps Enablement: Skilled in implementing CI/CD pipelines, observability standards, and modern engineering workflows to enhance pipeline reliability, agility, and operational efficiency.
Technical Standards & Frameworks: Experience in defining and enforcing data engineering best practices, architectural patterns, and reusable frameworks across multiple product teams.
Data Governance & Quality: Strong capability in embedding governance, data lineage, access control, and compliance requirements into engineering processes to ensure data integrity and trust.
Digital Analytics Expertise: Deep understanding of digital data sources and analytics tools related to customer journeys, digital engagement, and business performance measurement.
Strategic Revenue Management (SRM): Familiarity with using data to support pricing, promotions, and pack strategies that optimize revenue across multiple sales channels.
Marketing Mix Modelling (MMM): Knowledge of analytical models that evaluate marketing effectiveness across digital, e-commerce, and traditional channels to guide investment decisions.
Retail Business Solutions (RBS): Experience supporting or building tools that enhance retail execution, visibility, and performance tracking across both physical and online retail environments.
E-Route to Market (eRTM): Understanding of digital distribution strategies including Direct-to-Consumer (D2C), marketplaces, and data-enabled logistics and supply chain solutions.
Product-Based Model Implementation: Experience in adopting or supporting a product-based operating model for digital and data capabilities that are directly aligned to business outcomes.
Stakeholder Engagement: Ability to collaborate effectively with business and technical stakeholders, aligning on priorities and translating data strategy into tangible business value.
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