IT/Data Engineer

London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Analyst & Data Specialist

Senior Data Engineer

Senior Data Engineer - Databricks

Senior Data Engineer

Senior Data Engineer - Databricks

IT/ Data Engineer
Location- Central London
Salary- Up to £40,000
Experience level- 2 years

Duties and responsibilities - What you will be doing

  • Data Visualisation and reporting using Power BI Desktop.
  • Data Integration using Extract/Transform/Load processes from multiple data source (SQL, SharePoint, Salesforce, Excel, etc).
  • Supporting with several well-structured Data Analysis Projects, including interpreting briefs, scoping projects, carrying out data collection, cleaning and categorising data, analysing data and generating insights, and visualising and presenting results and making recommendations.
  • IT Applications Support - business applications and systems include: PIMRa (PLM), WinMan (ERP), REST API's, PixSell, Office 365, Active Directory and all systems integrations
  • Data consolidation & Preparation for PLM implementation
  • Ensure Jira ticketing system is kept up to date and staff receive timely updates on their requests
  • Liaise with 3rd party developers on new & existing projects
  • Set up and maintenance of client PCs, printers, and phones systems(3CX)
  • Updating documentation - methodologies, findings, and process.
    What we need from you
  • Good knowledge of PC hardware set-up and configuration
  • Knowledge of SQL Server to manipulate and analyse datasets
  • Proficiency in Power BI - designing and managing dashboards
  • Python experience would be desirable.
  • Strong computer skills including MS Office and Email (Advanced Excel skills are essential.)
  • Hands-on IT support - full range of Tier 1 and Tier 2 end-user support from service provisioning to retirement, including appropriate escalation where necessary
  • A passion for working with data, including high-quality and accurate work, and an ability to summarise key findings in simple terms.
  • Strong knowledge of Microsoft based operating systems (Windows 10/11)
  • Capability of working in a team and collaborating with and supporting colleagues.
  • The ability to change priorities quickly, and the capacity to handle multiple tasks in a fast-paced, changing environment
  • The ability to work independently but also with colleagues
  • A positive attitude and work ethic.

    Apply now or share your CV to (url removed)

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.