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Data Analyst
Role overview
The Data Analyst will prepare, analyse and communicate data to help AWTG and its clients make better decisions. The role involves working with data sources, producing reports and visualisations, and translating data into clear insight for delivery teams and stakeholders. You'll collaborate with multidisciplinary teams across data, AI, software engineering, product, QA and delivery to create practical outcomes for clients and end users.
Key responsibilities
- Collect, prepare, clean and manage data from a range of systems and sources.
- Analyse data using appropriate tools, techniques and quality assurance standards.
- Build reports, dashboards and visualisations that tell a focused and accessible story.
- Work with stakeholders to understand business questions and identify the data needed to answer them.
- Document analytical approaches, assumptions and limitations so outputs are repeatable and trusted.
- Support data governance, privacy, and ethical handling of data in line with project requirements.
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Essential skills and experience
- Experience preparing, analysing and presenting data for business or operational decision-making.
- Good working knowledge of Excel, SQL, BI tools or equivalent analytical platforms.
- Ability to identify data quality issues and prepare data so it is fit for purpose.
- Ability to communicate technical findings in plain language to non-technical stakeholders.
- Understanding of basic statistical or analytical techniques and quality assurance practices.
- Awareness of data protection, privacy, and secure handling of sensitive information.
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Desirable skills and experience
- Experience with Power BI, Tableau, Looker Studio, Python or R.
- Experience in digital products, public-sector services or consultancy environments.
- Knowledge of Agile delivery, Jira/Confluence, or multidisciplinary working.
What success looks like
- Analysis is accurate, timely, and clearly aligned to stakeholder questions.
- Dashboards and reports are understandable, useful and accessible.
- Data quality issues are identified early and handled transparently.
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