Data Analyst - INSIDE IR 35

LA International
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst/Data Scientist M/F/D

Data Analyst/Data Scientist M/F/D

Data Analyst/Data Scientist M/F/D

Data Analyst/Data Scientist M/F/D

Data Science Analyst / Graduate

Data Science Graduate

A Data Analyst with experience in Complex SQL, Advanced Excel and Data bases is required. This is a hybrid role with 2 days on site in Glasgow and is INSIDE IR35 so will require working via an FCSA accredited umbrella company.


You will be responsible for ensuring data quality and governance through strategic reporting, trend analysis, automation, and process improvements. The ideal candidate will have expertise in data quality controls, dashboard development, and integration of business data platforms. The role will involve collaborating with cross-functional teams, ensuring accurate data reconciliation, and driving enhancements in data governance models.

Key Responsibilities:
1.Data Quality and Controls:
oDevelop and maintain Data Quality Management Systems (DQMS) to ensure accuracy, integrity, and reliability of data.
oImplement data quality dashboards and expand reporting capabilities as required by Enterprise Process Technology (EPT) owners.
oOversee the implementation of Data Quality Dashboards 2.0, including history reporting and daily oversight mechanisms.
oPerform daily end-to-end reconciliation and develop check-sum control processes.
2.Trend Analysis & Control:
oMonitor alert volumes using trend check controls to detect anomalies and ensure early detection of data issues.
oUse statistical analysis to spot emerging data trends and recommend adjustments to improve accuracy.
3.Automation & Data Extraction:
oLead the automation of data extraction logic to improve efficiency and reduce manual intervention in reporting.
oCollaborate with digital and technology teams to automate reconciliation processes and streamline data workflows.
4.Governance & Integration:
oSupport the development and maintenance of the Data Quality Index (DQI) Operating Model, ensuring consistency in tracking and reporting of data metrics.
oManage centralized tracking tools for DQIs and ensure integration with Business Data Platforms (BDP) to support end-to-end data management.
oPartner with Change and Transformation teams to implement new data requirements and improve overall data governance.
5.Process Improvement & Risk Management:
oApply strategic thinking and business process re-engineering (BPR) methodologies to identify data-related challenges and opportunities.
oCollaborate with cross-functional teams to implement changes and improve data processes and controls.
oEnsure data risks are mitigated by implementing robust risk management strategies and control measures.

Key Skills & Competencies:
*Risk Management and Reporting: Ability to develop and implement reporting mechanisms that mitigate risks and enhance data integrity.
*Strategic Thinking: Strong analytical skills to align data analysis with business strategies and objectives.
*Business Process Re-engineering: Experience in process improvement and re-engineering to enhance data governance.
*Change Management: Skilled in managing organizational changes, especially within data and technology environments.
*Collaboration & Communication: Excellent written and verbal communication skills to work effectively with cross-functional teams.
*Technical Skills: Proficiency in data extraction tools, dashboard development (e.g., Tableau, Power BI), and automation techniques.
Qualifications:
*Bachelor's degree in Data Science, Computer Science, Information Technology, Business Analytics, or related fields.
*Proven experience with data quality management, dashboard development, and data automation.
*Strong knowledge of data governance models and data integration techniques.
*Familiarity with risk management practices and process improvement methodologies.



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 Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.