National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Senior Data Engineer and Platform Lead

Morgan Advanced Materials
London
1 day ago
Create job alert

Overview

Role: Senior Data Engineer and Platform Lead -Manufacturing

Location: Hybrid / Remote

Position: Staff

Morgan Advanced Materials is a world leader in the development and production of advanced materials. We produce a wide range of specialist, high specification materials that have extraordinary attributes and properties. Despite the complexity of our technologies, our focus is simple - we supply innovative, differentiated products made from advanced materials that enable our customers' products to perform more efficiently, more reliably and for longer.

Morgan is making a significant investment in establishing a world-class global data team, and we are excited to welcome new employees who will play a crucial role in this initiative. As part of this team, you will be instrumental in building the future data platform that will drive the success of our enterprise. Your contributions will not only shape the foundation of our data capabilities but also help position Morgan as a leader in leveraging data to achieve strategic business goals. This is a unique opportunity to be a part of a transformative journey, where your efforts will directly impact the future growth and innovation of the company.

Role: Senior Data Engineer and Platform Lead

Location: Hybrid / Remote

Position: Staff

We are looking for a highly skilled and experienced Azure Data Engineer to join our dynamic Global Data & Analytics Platform team. The successful candidate will be responsible for delivering scalable data solutions on the Global Data Platform, managing the platform, and driving its further development. This role requires proficiency in data engineering, data modelling, and data pipeline orchestration, with a proven track record of working with Azure-specific data platform tools and technologies. Strategic and forward-thinking capabilities, as well as experience in applying AI within a business data environment, are essential. Additionally, strong soft skills are necessary to collaborate effectively with cross-functional international teams and communicate complex technical concepts to non-technical stakeholders.

Responsibilities

Role Description:
Take ownership of the Azure-hosted analytical platform , ensuring it meets current and future business requirements.
Optimize the platform for performance, security, and scalability, ensuring alignment with best practices.
Design and develop scalable ETL pipelines and data solutions using Azure Data Factory, Synapse Analytics , and related tools.
Work closely with stakeholders to understand business needs and translate them into actionable data solutions.
Lead and mentor the Data & Analytics team, ensuring the smooth operation of data pipelines and alignment with organizational objectives.
Responsibilities:

Team Leadership and Coordination:
Provide technical guidance and mentorship to the Data & Analytics team.
Oversee the team's Agile processes including sprints, ask prioritisation and issue resolution
Define best practices for data pipeline design, development, and maintenance.
Coordinate with the solution architect to ensure platform architecture meets current and future requirements.
Act as a point of escalation for complex data engineering challenges.

Data Engineering:
Design, build, and maintain data pipelines for seamless integration of ERP and other critical data sources into Azure Synapse.
Ensure data workflows are optimized for performance, scalability, and reliability.
Lead efforts to standardize ETL/ELT processes and enforce data quality standards across the platform.
Implement data transformation logic to create clean, usable datasets for reporting and analytics.

Platform Administration and Optimization:
Monitor and optimize Azure Synapse Analytics performance, ensuring cost efficiency and resource utilization.
Implement monitoring and alerting for pipeline performance, failures, and resource consumption.
Suggest and implement automation for repetitive tasks using tools like Azure Logic Apps, Power Automate, or custom scripting.
Collaborate with the Data Governance Lead to ensure compliance with governance and security standards.

Business and Stakeholder Collaboration:
Work closely with business units to understand data requirements and ensure outputs align with business needs.
Validate that datasets and reports delivered by Power BI developers meet performance and usability standards.
Support data-driven decision-making by ensuring high-quality and accessible data across the organization.

Innovation and Strategy:
Stay updated on advancements in Azure, Synapse, data engineering tools and other relevant technologies to recommend and implement improvements.
Identify opportunities to leverage advanced data processing techniques (e.g., big data, real-time processing) and AI.
Contribute to the long-term strategy for the GDP, including scaling the platform for future needs.

Key Skills:
Leadership: Experience managing and mentoring data engineers or technical teams.
Technical Expertise:
Advanced knowledge of Azure Synapse Analytics, Azure Data Factory, and SQL.
Proficiency in ETL/ELT pipeline design and optimization.
Familiarity with automation tools (Azure Logic Apps, Power Automate, Python, or PowerShell scripting).
Knowledge of cloud performance optimization and cost management.
Collaboration: Strong ability to work cross-functionally with technical teams, governance leads, and business stakeholders.
Analytical Thinking: Ability to design efficient, scalable, and high-performing data workflows.
Strategy and planning: Experience with definition of data strategy, global data platform strategy and development roadmaps aligned with company's digital transformation plans.
Governance Awareness: Understanding of data governance principles and ability to work within established frameworks
Qualifications

EXPERIENCE & BACKGROUND

Qualifications/Experience
Bachelor or Master degree in a related field of study
5+ years of experience in data engineering, with a strong focus on Azure cloud services across a mixture of Enterprise and SME environments
Proficiency in Python, SQL, Azure Data Factory, Azure Synapse Analytics, Azure Data Lakes, and big data technologies like Apache Spark
Experience with DevOps practices and CI/CD pipelines in an Azure environment is a plus.
Certification in Azure (e.g., Microsoft Certified: Azure Data Engineer Associate) is highly desirable.
Excellent problem-solving abilities, strong communication skills, ability to work collaboratively in a team environment, and a keen attention to detail.
Fully up to speed with Agile development methodologies e.g., DevOps, Scrum (EPICs, Stories, Task, Issues, Bugs)
Excellent analytical and problem-solving skills and the ability to troubleshoot complex issues
Excellent stakeholder management, communication and interpersonal skills
Ability to thrive in a fast-paced and collaborative environment
Detail-oriented and well-organized
Morgan Advanced Materials is an EEO/AA/M/W/D/V Employer Ind-1
#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Engineer

Junior/Mid/Senior Data Engineer - Hybrid, London

Junior/Mid/Senior Data Engineer - Hybrid, London

Junior/Mid/Senior Data Engineer - Hybrid, London

Junior/Mid/Senior Data Engineer - Hybrid, London

Junior/Mid/Senior Data Engineer - Hybrid, London

National AI Awards 2025

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.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.

Machine Learning Jobs UK 2025: 50 Companies Hiring Now

Bookmark this page—we refresh the Hotlist every quarter so you always know who’s really scaling their ML teams. The UK’s National AI Strategy, a £2 billion GenAI accelerator fund and a record flow of private capital have kicked ML hiring into overdrive for 2025. Whether you build production‑grade LLM services or optimise on‑device models for edge hardware, employers need your skills now. Below you’ll find 50 organisations that advertised UK‑based machine‑learning vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the type of employer—and mission—that excites you. For each company we list: Main UK hub Example live or recent vacancy Why it’s worth a look (stack, impact, culture) Search any employer on MachineLearningJobs.co.uk to see real‑time adverts, or set a free alert so fresh openings drop straight in your inbox.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.