Designed and maintained Microsoft Fabric data pipelines to ingest, transform, and validate workforce and vendor data from multiple source systems, replacing manual consolidation workflows and applying data quality checkpoints throughout.

Defined data mapping logic and transformation rules for semantic models powering a Power BI transparency dashboard, ensuring consistent, governed reporting for clients and vendors.

Developed Python scripts to prepare and transform datasets for strategic workforce planning decisions, documenting data flows and transformation specifications for business stakeholders.

Engaged with business stakeholders to gather reporting requirements and translate them into Power BI dashboard designs, facilitating iterative feedback cycles to validate solution accuracy.

Automated daily business processes using Power Apps dataflows and designed timesheet automation solutions, reducing manual effort and improving data freshness and operational efficiency.

Conducted market research analysis of vendor pay rates across multiple roles by building an AI agent using Microsoft Copilot that automated data retrieval from a manually curated database, improving process efficiency by 73% compared to manual data scraping and enabling faster, more accurate benchmarking decisions.

Led data migration activities from UKG into a centralised Microsoft Fabric Lakehouse, defining ETL mapping specifications, data cleansing rules, and transformation logic for payroll, rostering, and people services data.

Documented integration specifications and data mapping artefacts to support cross-system data flows, enabling consistent and auditable reporting for business stakeholders.

Conducted system integration testing within UAT environments and coordinated the implementation of validated configurations into production, ensuring compliant and high-quality data outcomes.

Implemented data governance practices including validation checkpoints, configuration risk assessments, and quality controls to ensure data accuracy and process compliance.

Managed ad-hoc analysis tasks through JIRA, providing prescriptive reports and system configuration recommendations to resolve rostering and payroll issues.

Developed detailed financial and KPI reporting using Advanced Excel dashboards with variance analysis, translating operational data into actionable insights for senior management decision-making.

Contributed to the planning and migration of KPI reporting to SharePoint, performing migration validation and testing that resulted in a 22% reduction in reporting time.

Documented data reconciliation processes for payroll data using Excel and UKG, standardising procedures for casual and permanent employee financial data reconciliation.

Analysed rostering discrepancies and produced prescriptive reports to improve payroll processes, identifying data quality issues and recommending corrective actions.

Collaborated with cross-functional teams to identify data sources, streamline data collection processes, and resolve data quality inconsistencies, achieving a 21% improvement in data accuracy and a 26% reduction in KPI report development time.

Conducted data quality assessments and audits using Dataforce Runabout, identifying and resolving input inconsistencies to support reliable analytical reporting.

Developed performance and benchmarking reports using Power BI and SQL, translating complex data into clear recommendations for non-technical stakeholders.

Analysed social media data via Twitter API and R Studio to support content strategy decisions, driving measurable improvements in engagement and customer acquisition.

Engineered an R-Shiny analytics platform integrating real-time data from 800+ air quality stations, reducing data analysis time by 60% and improving data accuracy by 85%.

Developed a web application with automated credit estimation using Linear Regression, and prepared standardised water quality data visualisations for a national pollution control board using Tableau.

Conducted exploratory analysis and time series modelling using R Studio for energy generation prediction, supporting proof-of-concept planning with stakeholders.