Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

PNE Data Foundations Sr. Lead

Mars IS UK
Windsor
6 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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.