PNE Data Foundations Sr. Lead

Mars IS UK
Windsor
9 months ago
Applications closed

Job Description:

The PNE Data Foundations Sr Lead ensures that Pet Nutrition Europe (PNE) has a strong, governed, and scalable data foundation to enable high-quality analytics and insights. This role is critical in bridging the gap between the AOE D&A data team and PNE, ensuring alignment with global data models, governance principles, and strategic data initiatives such as the DDF (Digital Data Foundation) program.

What are we looking for?

  • Preferred education Is a university degree In business or IT

  • Strong background in data engineering, data governance, and data architecture, ideally in a multinational or matrixed organization.

  • 5+ years of experience in data engineering, analytics, or data management, with hands-on expertise in cloud-based data platforms (e.g., Azure, AWS, GCP, Snowflake).

  • Proven expertise in designing, developing, and maintaining scalable data pipelines, ETL/ELT processes, and integrations to support advanced analytics.

  • Experience with data governance frameworks, master data management (MDM), metadata management, and ensuring data compliance with global standards.

  • Deep understanding of SQL, Python, Spark, or other relevant data processing technologies used for data transformation and analytics enablement.

  • Familiarity with modern data architectures, including data lakes, data warehouses, and data mesh principles.

  • Experience working with global and regional teams to align on data strategy, governance, and best practices.

  • Strong knowledge of data quality frameworks and best practices for ensuring high integrity, reliability, and consistency of data assets.

What will be your key responsibilities?

  • Data Governance & Compliance:Ensure all PNE data assets follow the governance frameworks, data quality standards, and security policies defined by the AOE D&A data team.

  • Data Model & Architecture Alignment:Ensure that analytics solutions within PNE fully leverage the data models, governance structures, and best practices established at the AOE level.

  • Data Engineering & Infrastructure:Oversee the design, development, and maintenance of data pipelines, integrations, and ETL processes to ensure efficient data flow and accessibility for analytics use cases.

  • Collaboration & Stakeholder Management:Act as the key connection between AOE D&A and PNE, facilitating knowledge-sharing, alignment, and implementation of strategic data initiatives, including the DDF program.

  • Data Platform Optimization:Work closely with AOE data teams and PNE analytics teams to optimize the data infrastructure, ensuring performance, scalability, and cost efficiency.

  • Metadata & Asset Management:Drive consistent metadata management and data asset governance, ensuring data reliability, accessibility, and standardization across PNE.

  • Enablement & Best Practices:Educate and support the PNE teams in data stewardship best practices, ensuring they effectively leverage governed data assets and self-service capabilities.

  • Monitoring & Data Quality Assurance:Implement data validation, lineage tracking, and anomaly detection mechanisms to ensure high data quality across PNE analytics initiatives.

What can you expect from Mars?

  • Work with diverse and talented Associates, all guided by the Five Principles.
  • Join a purpose driven company, where we’re striving to build the world we want tomorrow, today.
  • Best-in-class learning and development support from day one, including access to our in-house Mars University.
  • An industry competitive salary and benefits package, including company bonus.

#TBDDT

Mars is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. If you need assistance or an accommodation during the application process because of a disability, it is available upon request. The company is pleased to provide such assistance, and no applicant will be penalized as a result of such a request.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.