Data Engineer

In Technology Group
Glasgow
5 days ago
Create job alert

Job Title:Data Engineer


Location:Glasgow ( Hybrid 2 days on-site)


Salary: £40,000 – 50,000 D.O.E


About the Company:Our client, a prominent manufacturing firm based in Glasgow, is committed to innovation and excellence in their field. They are currently seeking a skilled Data Engineer to join their team and help drive data-driven decision-making processes to enhance their manufacturing operations.


Job Description:As a Data Engineer, you will be instrumental in designing, developing, and maintaining the data infrastructure. You will work closely with data analysts and other stakeholders to ensure data is accessible, reliable, and secure.

  • Building and optimising data pipelines and architectures.
  • Developing data integration solutions to connect various data sources.
  • Implementing data quality and governance practices.
  • Collaborating with cross-functional teams to understand data requirements.
  • Ensuring the security and privacy of data.


Key Responsibilities:

  • Design and develop scalable data pipelines.
  • Integrate data from multiple sources into a unified data warehouse.
  • Monitor and troubleshoot data pipeline performance.
  • Implement data quality checks and validation processes.
  • Collaborate with data analysts to support their data needs.
  • Maintain and optimise database systems.


Qualifications:

  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • Proven experience as a Data Engineer or in a similar role.
  • Proficiency in SQL, Python, and ETL tools.
  • Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).
  • Strong understanding of data warehousing concepts.
  • Excellent problem-solving skills and attention to detail.


Benefits:

  • Competitive salary and benefits package.
  • Opportunities for professional development and growth.
  • Collaborative and inclusive work environment.
  • Flexible working hours and remote work options.

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.