England, United Kingdom
I’m a Data Analyst and recent MSc Data Science & AI graduate from the University of Liverpool, with a passion for transforming raw data into impactful insights. With hands-on experience in financial analytics, data governance, and stakeholder-driven reporting, I thrive at the intersection of data, business strategy, and technology. At Small Cap Networks and Sensys Technology, I’ve developed and optimized end-to-end data workflows using Python, SQL, Power BI, and Excel—automating processes, improving data quality, and building dashboards that drive real decision-making. I’m particularly drawn to roles that involve improving data infrastructure, supporting governance standards, and applying machine learning for predictive insights. Whether working independently or with cross-functional teams, I bring strong communication, a growth mindset, and a deep curiosity for solving complex problems through data. 🔍 Interests: Data Governance | Financial & Risk Analytics | Predictive Modeling | Business Intelligence | Sports Analytics 💡 Tools & Languages: Python | SQL | Power BI | Excel | GitHub | MySQL
Delivered actionable business insights by analysing multi-source operational data using SQL and Python, translating complex findings into clear recommendations that improved decision-making quality by ~10% across commercial and operations teams. Designed, maintained, and iterated interactive Power BI dashboards tracking 30+ KPIs, ensuring stakeholders had real-time visibility into performance trends, risks, and opportunities; proactively incorporated feedback from non-technical users to improve usability and clarity. Built and automated data cleaning, reconciliation, and validation workflows using Python, SQL, and Excel, achieving 100% data accuracy while reconciling £2,000–£3,000 in daily revenue, strengthening governance and trust in reporting outputs. Developed automated inventory and demand analysis pipelines, reducing stock shortages by 15% across 50+ raw materials and products, and enabling data-driven planning through repeatable, scalable analytical processes. Performed customer behaviour analysis and segmentation to support marketing and growth initiatives, improving campaign ROI by 12% by clearly linking analytical insights to commercial outcomes and decision-making. Acted as a trusted analytics partner to cross-functional teams (operations, marketing, finance), demonstrating strong stakeholder communication, prioritisation, and problem-solving skills, while documenting processes and promoting best practices for sustainable analytics delivery.
Supported daily and monthly financial reporting by extracting, cleaning, and analysing transactional data using SQL and Excel, ensuring timely and accurate inputs for management accounts and performance reviews. Developed and maintained Power BI reports focused on P&L performance, cost drivers, revenue trends, and variance analysis, tailoring dashboards for finance leaders with differing levels of technical expertise. Automated routine finance processes using Python, including data preprocessing, validation checks, and report refresh workflows, reducing manual effort and improving consistency across recurring analyses. Performed ad-hoc financial and operational analysis to answer stakeholder queries, translating ambiguous business questions into structured analytical tasks and clearly communicating findings and assumptions. Worked collaboratively with finance, operations, and compliance stakeholders, demonstrating strong attention to detail, documentation discipline, and stakeholder management, while adhering to data governance and confidentiality standards.
Analysed operational and product usage datasets from multiple business applications (e.g., payroll, tax, CRM systems) using SQL and Excel, supporting product performance tracking and internal reporting. Built and maintained Power BI dashboards that visualised key software metrics, client engagement trends, and process KPIs, enabling product and customer success teams to make evidence-based decisions. Automated data extraction, transformation and loading (ETL) tasks using Python scripts, improving the reliability and timeliness of data ingestion across internal reporting and analytics workflows. Performed detailed data cleaning, validation and quality assurance using SQL and Python to ensure accuracy and consistency in client and system records feeding into business intelligence reports. Contributed to the development and optimisation of data pipelines, including querying data stored in internal databases, preparing structured datasets for analysis, and documenting data flows for team use. Supported cross-functional teams including product development, support, and sales by turning analytical findings into concise insights and recommendations, demonstrating strong communication, problem-solving, and stakeholder collaboration skills. Assisted in preparing ad-hoc analytical reports and operational forecasts, leveraging Excel modelling and SQL queries to inform management on trends, risks, and opportunities within product usage and customer behaviour.
Wrote and optimised complex SQL queries, stored procedures, functions, and triggers to support efficient data retrieval and application functionality across internal systems and client deliverables. Maintained and supported relational databases (e.g., MySQL/SQL Server) with a strong focus on performance tuning, data integrity, backup, recovery, and security. Worked closely with product and business teams to translate analytical and reporting needs into reliable data solutions and SQL-driven reports, ensuring clarity and accuracy for stakeholders. Assisted in ETL design, database schema improvements, and optimisation of data structures, improving query performance and reducing processing time. Used SQL for data cleaning and transformation to support downstream reporting and analytics workflows, ensuring consistency and quality across datasets. Collaborated effectively with cross-functional teams including developers, analysts, and project managers demonstrating strong communication, problem-solving, and documentation skills. Identified and resolved database performance issues through indexing and query optimisation, reinforcing system reliability and responsiveness under operational loads.
Extracted, cleaned, and analysed structured data from Sensys’s internal systems and client-facing products using SQL and Excel, delivering accurate datasets for reporting and insights. Built and maintained Power BI dashboards that visualised software performance metrics, customer engagement, and operational KPIs, enabling teams to track trends and inform decisions. Automated routine data tasks and validation checks using Python scripting, improving efficiency and reducing manual effort while reinforcing data quality and governance. Supported development and upkeep of data pipelines and reporting workflows, ensuring reliable data delivery to business intelligence tools and teams. Produced ad-hoc analytical reports and assisted in data modelling to answer stakeholder queries with clear, actionable insights that enhanced product and operational understanding. Communicated analytical findings effectively to technical and non-technical stakeholders, demonstrating strong collaboration, attention to detail and problem-solving skills. Documented analysis processes, assumptions, and results to support reproducibility, knowledge sharing, and long-term team enablement.