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- Tasks: Lead data engineering teams to build scalable data platforms and drive data-driven solutions.
- Company: Join Mars, a leader in pet nutrition with a focus on innovation and digital transformation.
- Benefits: Competitive salary, health benefits, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact in the pet nutrition industry by leveraging data and analytics.
- Qualifications: Proven leadership in data engineering and experience with DataOps and DevOps practices.
- Other info: Collaborative environment with a strong emphasis on career development and innovation.
The predicted salary is between 60000 - 84000 £ per year.
Are you passionate about Data and Analytics (D&A) and excited about how it can transform the way an enterprise works? Do you have the strategic vision, technical expertise, and leadership to drive data-driven solutions? 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 a vibrant category in the FMCG sector. The Digital First program, mobilized by the Mars Pet Nutrition leadership team, centers pet parents and digitalizes a wide range of business processes to achieve ambitious targets in top‑line growth, earnings, and pet parent centricity. The role supports the Digital First agenda by developing and maintaining robust data pipelines and storage solutions to support data analytics and machine learning initiatives, reporting to the Director-Data Engineering Solutions, and operating globally in collaboration with engineering teams across growth products.
Responsibilities
- Technical Leadership: Provide strong technical leadership to data engineers and DevOps engineers across growth product teams. Partner in the design, implementation, and evolution of scalable data platforms and assets. Champion data engineering best practices and foster a collaborative, high-performance culture.
- 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. Be accountable for technical decisions and architectural direction while enabling 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 and 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 that maximize value through data utilization.
Knowledge / Experience
- Data Engineering Leadership: Ability to lead global technical teams in building scalable data platforms, data pipelines, and infrastructure that support 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: 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 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.
Product Engineer Lead (Demand) employer: Mars
Contact Detail:
Mars Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Product Engineer Lead (Demand)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and achievements. This is a great way to demonstrate your technical expertise and passion for data engineering to potential employers.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to data engineering and leadership. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Mars.
We think you need these skills to ace Product Engineer Lead (Demand)
Some tips for your application 🫡
Show Your Passion for Data: Make sure to highlight your enthusiasm for data and analytics in your application. We want to see how you can transform data into actionable insights, so share any relevant experiences or projects that showcase your passion!
Tailor Your Application: Don’t just send a generic CV and cover letter! Tailor your application to reflect the specific skills and experiences mentioned in the job description. We love seeing candidates who take the time to align their background with our needs.
Highlight Technical Leadership: Since this role involves leading technical teams, be sure to emphasise your leadership experience in data engineering or related fields. Share examples of how you've guided teams to success and fostered a collaborative culture.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Mars
✨Know Your Data Inside Out
Make sure you’re well-versed in the data engineering principles and practices mentioned in the job description. Brush up on your knowledge of data pipelines, CI/CD processes, and DataOps. Being able to discuss specific examples from your past experience will show that you can lead technical teams effectively.
✨Showcase Your Leadership Skills
Prepare to talk about your experience in leading global technical teams. Think of instances where you’ve championed best practices or fostered a collaborative culture. Highlight how you’ve driven projects to success while ensuring quality and consistency across solutions.
✨Engage with Stakeholders
Be ready to discuss how you’ve collaborated with various stakeholders in previous roles. Share examples of how you’ve aligned data priorities with business goals and maximised value through data utilisation. This will demonstrate your ability to engage effectively with both technical and non-technical teams.
✨Understand the Business Context
Familiarise yourself with the FMCG sector and Mars’ Digital First programme. Knowing how data analytics can transform business processes will help you articulate your vision for the role. Be prepared to discuss how your skills can contribute to achieving ambitious targets in top-line growth and pet parent centricity.
