Senior Data Analyst - TD Bank - Toronto, Ontario, Canada
(2025-07)
Environment: SQL queries, ETL pipelines, Power BI, Tableau, Python, R, AWS Redshift, Snowflake, Data bricks.
- Designed and optimized complex SQL queries and scalable ETL pipelines to process large-scale financial, retail, and supply chain datasets, enabling accurate and timely business insights.
- Developed and delivered interactive dashboards and executive reports using Power BI and Tableau to track KPIs, portfolio performance, and operational metrics across business units.
- Built predictive analytics and time-series forecasting models (ARIMA, Prophet, LSTM) using Python and R to improve demand forecasting, risk assessment, and financial planning.
- Integrated and managed data across cloud platforms (AWS Redshift, Snowflake, Data bricks) to support scalable data warehousing and advanced analytics.
- Implemented data quality frameworks, validation rules, and governance standards to ensure data accuracy, consistency, and regulatory compliance in financial reporting.
- Collaborated with cross-functional stakeholders (finance, risk, operations, marketing) to design data-driven solutions, automate reporting workflows, and enhance decision-making processes.
- Applied machine learning algorithms (classification, regression, clustering) to analyse customer behaviour, detect anomalies, and support segmentation strategies.
- Leveraged NLP techniques and text analytics to extract insights from unstructured financial data, reports, and client communications.
- Utilized Generative AI (GenAI) and Large Language Models (LLMs) to automate report generation, enhance data storytelling, and accelerate insight discovery.
- Improved development efficiency by adopting GitHub Co-pilot, Git, and Agile methodologies, enabling faster delivery, code quality, and continuous integration of analytical solutions.
Data Analyst - Aviva - Toronto, Ontario, Canada
(2022-04 - 2025-06)
Environment: Oracle SQL, PostgreSQL, Informatica, Talend, Tableau, QlikView, SAS, R, Python, Azure Synapse, ETL, Insurance Analytics, Data Governance
- Developed scalable enterprise data models using Oracle SQL and PostgreSQL to support insurance reporting, policy analytics, claims processing, financial analysis, and operational performance tracking at Aviva.
- Designed and maintained ETL pipelines using Informatica and Talend to integrate policy, claims, customer, finance, and underwriting data into centralized analytics platforms for enterprise reporting.
- Built interactive dashboards and business intelligence reports using Tableau and QlikView to monitor KPIs including claims ratios, policy renewals, customer retention, underwriting performance, and operational efficiency.
- Performed advanced statistical analysis and predictive modelling using SAS and R to analyse customer behaviour, claims trends, fraud indicators, risk exposure, and actuarial forecasting models.
- Implemented data validation, governance controls, and audit frameworks to ensure data accuracy, integrity, and compliance with insurance industry regulations, PCI-DSS, and enterprise security standards.
- Automated reporting, reconciliation, and data processing workflows using Python and PowerShell, improving reporting efficiency, reducing manual effort, and accelerating business decision-making.
Data Analyst - GSK - Mumbai, India
(2020-08 - 2021-12)
Environment: SAS, Python, R, SQL Server, Tableau, Excel VBA, PowerShell, Talend, ETL, Data Mining, Risk Modeling, Statistical Analysis, Pharmaceutical Analytics, Data Governance
- Performed advanced pharmaceutical data analysis and statistical modelling using SAS and Python to evaluate drug sales trends, patient adherence, market performance, and supply chain efficiency across healthcare operations at GlaxoSmithKline.
- Developed and maintained interactive Tableau dashboards and Excel VBA-based reporting solutions to monitor commercial performance, inventory utilization, manufacturing KPIs, and operational metrics for business leadership.
- Built predictive risk models and analytical scorecards using R and SQL Server to support operational risk assessment, demand forecasting, and strategic planning initiatives within pharmaceutical and healthcare business units.
- Automated reporting and data processing workflows using Python and PowerShell, improving reporting accuracy, reducing manual intervention, and accelerating business reporting cycles.
- Integrated multi-source enterprise data from ERP, CRM, clinical, manufacturing, and supply chain systems using Talend ETL pipelines, enabling centralized analytics and enterprise-wide reporting capabilities.
- Ensured data governance, audit readiness, and compliance with pharmaceutical industry standards by implementing validation checks, data quality monitoring, and secure reporting processes.
Data Analyst - General Mills - Mumbai, India
(2019-06 - 2020-07)
Environment: Python, Apache Airflow, ETL, Power BI, Looker, R, Scikit-learn, AWS S3, Amazon Redshift, Snowflake, SQL Server, JSON, SQL
- Developed and managed enterprise ETL pipelines using Python and Apache Airflow to process large-scale supply chain, manufacturing, sales, and inventory data across global business operations at General Mills.
- Built interactive Power BI and Looker dashboards to track production efficiency, demand forecasting, order fulfilment, retail sales trends, and operational KPIs for business and supply chain teams.
- Applied predictive analytics and machine learning models using R and Scikit-learn to improve demand planning, inventory optimization, and pricing strategy across FMCG product categories.
- Integrated structured and semi-structured data sources, including REST APIs and JSON datasets, into centralized analytics platforms to enable near real-time business reporting and operational visibility.
- Utilized AWS S3, Amazon Redshift, Snowflake, and SQL Server to design scalable cloud-based data warehousing and reporting solutions supporting enterprise analytics initiatives.
- Ensured data governance, validation, and reporting accuracy by implementing audit controls, standardized ETL processes, and data quality monitoring frameworks.
- Supported analytics modernization initiatives by optimizing SQL queries, improving ETL performance, and enhancing data accessibility for enterprise reporting and executive dashboards.
