Data Engineering Lead - Growth

myGwork
London
1 year ago
Applications closed

Related Jobs

View all jobs

BI and Data Engineering Lead

Lead Data Engineer

Data Science Manager – Property Tech – London

Data Science Manager – Property Tech – London

Data Engineering Manager

Head of Data Engineering (AI)

This job is with Mars, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ business community. Please do not contact the recruiter directly. 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 of Senior Director, Data Foundations, 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 across core and growth functions Key Responsibilities Please list the most important and relevant responsibilities Leadership and Team Management: Lead and mentor a team of data engineers and DevOps engineers. Provide guidance and support in the design, implementation, and maintenance of data assets. Foster a collaborative and high-performance team culture focused on innovation and excellence Data Asset Delivery : Drive the end-to-end delivery of data products. Collaborate closely with cross-functional teams to understand business requirements and translate them into technical solutions. Ensure timely and accurate delivery of data products that meet business needs and quality standards. DataOps and Optimization : Implement DataOps practices to streamline data engineering workflows and improve operational efficiency. Automate data pipeline deployment and monitoring using CI/CD tools. Technical Leadership: Provide technical leadership and guidance on data engineering best practices. Stay informed about industry trends and emerging technologies in data engineering and analytics. Standardization and Governance: Ensure adherence to data governance policies, procedures, and standards. Implement best practices for data management, security, and compliance. Promote data quality and integrity across all data products. Monitor data pipeline performance and optimize for scalability, reliability, and speed. 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. Job Specifications /Qualifications State the preferred education, knowledge, skills and experience this position requires. State the physical and/or mental requirements for the role (e.g. stand for x hours, lift x weight, concentration on repetitive tasks). Note: May differ from the current job holder's own skills and experience. Education & Professional Qualifications 8 years' experience as a Data Engineer. Knowledge / Experience Experience with Spark, Databricks, or similar data processing tools. Strong technical proficiency in data modeling, SQL, NoSQL databases, and data warehousing. Hands-on experience with data pipeline development, ETL processes, and big data technologies (e.g., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Google Cloud, and cloud-based data services (e.g., AWS Redshift, Azure Synapse Analytics, Google BigQuery). Experience with DataOps practices and tools, including CI/CD for data pipelines. Excellent leadership, communication, and interpersonal skills, with the ability to collaborate effectively with diverse teams and stakeholders. Strong analytical and problem-solving skills, with a focus on driving actionable insights from complex data sets. Experience with data visualization tools (e.g., PowerBI). Proficiency in Microsoft Azure cloud technologies would be a bonus. Key Mars Leadership Competencies (4-6) Refer to the Mars Talent and Development Library Note: competencies selected should be job related Communicates effectively Collaborates Drives Results Self-Development Key Functional Competencies & Technical Skills (3-5) Refer to the Mars Talent and Development Library Distinguish any preferred competences at the end of the list & notate them as "preferred" Data Modeling: Expertise in conceptual, logical, and physical data modeling, with an emphasis on designing scalable and efficient data structures. ETL Development: Proficiency in building and maintaining ETL processes, including data ingestion, transformation, and integration. Cloud Platforms: Proficiency in using cloud platforms like AWS, Azure, or Google Cloud for data storage, processing, and analytics. Database Management: Strong knowledge of both relational and non-relational database systems, including SQL and NoSQL databases. DataOps Practices: Experience with CI/CD for data pipelines and automating data engineering workflows to improve efficiency and reliability. Data Governance: Understanding of data governance principles, including data quality, metadata management, and regulatory compliance. 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

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 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.