Data Analytics Apprentice - Data Analytics & Business Intelligence

Apprentice – Data Analytics (CSDD) 

Overview 

The Apprentice – Data Analytics supports the day-to-day execution of data analytics and reporting activities within the Content and Student Digital Delivery (CSDD) team. Working under the close direction of the Lead Specialist, Data Analytics & Business Intelligence, the role focuses on preparing, cleaning, and analysing data, and supporting the build of dashboards and reports with accuracy, consistency, and a commitment to quality. 

This role is designed for individuals who are building their foundational skills in data analytics, demonstrating analytical curiosity, attention to detail, and a willingness to learn. The Apprentice follows established analytical workflows and standard operating procedures (SOPs), maintains accurate data records, and escalates issues promptly within defined channels. Exposure to Python for data manipulation and to AI-driven or automation projects is a core part of this role. 

The ideal candidate has up to 1–2 years of relevant experience or equivalent internship exposure. Prior experience in data analytics, business intelligence, reporting, or AI/automation project environments is an advantage. 

Key Responsibilities 

Data Preparation & Analysis 

Collect, clean, and transform data from multiple sources using Python (e.g., pandas) and SQL, following established analytical workflows. Perform exploratory analysis, produce accurate outputs within agreed timelines, and support the wider team during key reporting and delivery cycles. 

Dashboarding & Reporting 

Support the build and maintenance of dashboards and recurring reports in Power BI, Excel, or equivalent tools. Ensure metrics, definitions, and visualisations are accurate, consistent, and aligned with PMO governance standards, and provide timely status updates to the supervisor. 

AI & Automation Project Support 

Contribute to AI and automation initiatives – such as reporting automation, Copilot/agentic pilots, and workflow optimisation – by preparing datasets, testing outputs, documenting results, and supporting proof-of-concept work under guidance. Apply consistent judgement when reviewing AI-generated outputs for accuracy and quality. 

Process Documentation & Record Keeping 

Follow documented analytical workflows and standard operating procedures. Maintain accurate data records, query logs, and version-controlled outputs in SharePoint or designated repositories. Assist in updating Job Aids and process documentation as directed. 

Issue Identification & Escalation 

Identify routine data quality or analytical issues during task execution and escalate promptly to the supervisor with sufficient context for rapid resolution. Distinguish expected data variation from non-routine anomalies that require review. 

Stakeholder Communication 

Communicate analysis status, findings, and workflow updates clearly and consistently to the immediate supervisor. Participate constructively in team check-ins, reviews, and analytics working sessions. 

Qualifications & Experience 

• Bachelor’s degree in a quantitative, computing, or analytical discipline (e.g., Data Science, Computer Science, Statistics, Mathematics, Engineering), or equivalent practical experience. 

• Up to 1–2 years of relevant work experience. Candidates with relevant internship or placement experience will be considered. 

• Working knowledge of Python for data manipulation and analysis and automation 

• Exposure to AI or automation projects – whether academic, internship, or professional – is required. 

• Prior exposure to data analytics, business intelligence, reporting, or digital operations environments is preferred. 

• Strong written and verbal communication skills, with the ability to provide clear and structured updates to supervisors. 

• High attention to detail, analytical diligence, and the ability to manage concurrent priorities within defined timelines. 

Required Skills & Competencies 

• Analytical thinking: Ability to interpret data, spot patterns and anomalies, and translate findings into clear, accurate outputs. 

• AI/automation awareness : Familiarity with AI, machine learning, or automation concepts and a willingness to apply them to reporting and workflow tasks. 

• Attention to detail : careful adherence to analytical procedures, governance requirements, and data quality expectations. 

• Learning orientation : willingness to absorb new tools, techniques, and domain knowledge quickly and apply them as directed. 

• Data tooling comfort : working with data, documentation, and reporting/BI tooling in a structured operational environment. 

• Team player with a positive, collaborative approach to reviews, check-ins, and cross-functional working sessions. 

• Clear and timely communication of analysis status and issues to supervisors. 

Tools & Systems 

Candidates should have working knowledge of, or a demonstrated willingness to learn, the following: 

• Python (basics) – data cleaning, transformation, and analysis 

• Querying and extracting data from Looker, Oracle PDH,JIRA 

• Power BI / Excel – dashboarding, visualisation, and reporting 

• AI & automation tools – Microsoft Copilot Studio, agentic/AI reporting solutions, and workflow automation 

• JIRA & Smartsheet – task tracking, workflow management, and operational trackers 

• Microsoft Teams, Planner & SharePoint – collaboration, file management, and cross-team communication 

Success Metrics 

Effectiveness at this level is demonstrated by: 

• Consistent and accurate completion of assigned analytical tasks within agreed timelines, following established workflows and SOPs. 

• Low error rate and minimal rework on data preparation, analysis, and reporting outputs. 

• Reliable, accurate dashboards and reports with correct metrics, definitions, and version control. 

• Constructive contribution to AI and automation pilots, with well-documented and tested outputs. 

• Timely and appropriately contextualised escalation of data quality or analytical issues to the supervisor. 

• Positive feedback from the supervisor on reliability, analytical rigour, and attention to detail. 

• Constructive participation in team check-ins, reviews, and retrospectives.