Senior Engineer - Data Engineer (Manufacturing Design Systems)

Queens University
Belfast
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Engineering Data Analyst

Senior Civil Engineer

Senior Engineering Manager - HVDC Control & Protection

Senior Engineering Manager - HVDC Control & Protection

Senior Structural Engineer

Lead Senior Structural Engineer

The Advanced Manufacturing Innovation Centre are seeking engineers who want to innovate and apply their knowledge to the challenges of industry and society to support Digital Design and Manufacturing Engineering activities within AMIC. You will apply your specialist knowledge and experience of methods and processes, to generate innovative research outputs which have a direct economic and technical benefit to companies and sectors. You will work collaboratively with your team, industry, technology providers, national technology centres and academia to deliver key projects focused on advanced manufacturing. About the person: The successful candidate must have, and your application should clearly demonstrate that you meet the following criteria: Honours degree or equivalent in computing, engineering or a related discipline with significant relevantindustrial experience OR minimum HND in a related discipline with extensive recent and relevant industrial experience Recent relevant experience as a data integration engineer within an industrial or R&D setting, preferably witha focus on engineering design and product data management. Demonstrable proficiency in data transformation and analytics techniques to harmonise and understandstructured, semi-structured, and unstructured datasets Strong knowledge of ETL tools and data integration platforms Proficiency in SQL and experience with relational databases Demonstrable hands-on experience with programming and scripting highlighting evidence of one or more ofthe following: Strong skills in Python, Java, or C# for developing integration workflows. Proficiency with data manipulation libraries like Pandas, NumPy, and data visualisation tools. Scripting expertise (e.g., Bash, PowerShell) for automating integration tasks. Demonstrable evidence of data integration between IT/OT domains, preferably with a focus on design and manufacturing systems (e.g. CAD, CAM, MES, ERP, PLM, etc). To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information document on our website. Skills: Senior Data Engineer Benefits: Work From Home

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.