Quality Data Analyst

About the role

The Quality Data Analyst will transform inspection, audit, NCR, rework, supplier, completion and document data into reliable insights that enable project and operations leaders to prioritise risk, improve performance and prevent recurrence.

Key responsibilities

• Define quality data requirements, KPI definitions, coding rules, data ownership, reporting frequency and validation checks.
• Consolidate and cleanse data from inspection requests, ITPs, NCRs, audits, weld/NDT registers, coating reports, test packs, supplier records and handover systems.
• Build and maintain dashboards, scorecards, trend reports, Pareto analyses, ageing reports and early‑warning indicators.
• Analyse defect, rework, repair, overdue action, audit, vendor and first‑pass acceptance trends by discipline, contractor, system, location and period.
• Validate source‑to‑report traceability and challenge incomplete, duplicated or inconsistent data before publication.
• Partner with QA/QC and delivery teams to investigate adverse trends, quantify causes, impacts and action effectiveness.
• Prepare weekly and monthly quality performance packs, management‑review inputs and concise narratives explaining material changes.
• Automate repeatable data preparation and reporting while retaining transparent, auditable calculations and source links.

Required profile

• Bachelor’s degree or HND in Engineering, Quality, Data/Statistics, Information Systems or related discipline.
• At least 5 years of quality, inspection, project‑controls or operational data experience, including 2 years of analytics/reporting responsibility.
• Strong spreadsheet and dashboard capability; experience with databases, BI tools or scripting is advantageous.
• Solid understanding of QA/QC processes, inspection/NCR lifecycles, audit data and supplier quality.
• Excellent data‑quality discipline, analytical reasoning, visual communication and technical writing.

Required skills

• Spreadsheet tools (e.g., Excel)
• Dashboard development and visualisation
• Database querying
• Business Intelligence tools (e.g., Power BI, Tableau)
• Scripting for data automation