Afeez Sanni

Designed and optimised ETL pipelines using Azure Data Factory and Databricks, reducing data processing time by 30% and improving reporting availability for business-critical insights. Tuned Snowflake SQL queries with clustering, partitions, and materialised views, boosting query performance by 25% and cutting dashboard refresh times. Automated ingestion and transformation workflows with Python and SQL, lowering manual intervention and saving 15+ hours per week in repetitive tasks. Built and managed Fivetran connectors for ELT, enabling reliable replication from cloud applications into Snowflake with 99.9% pipeline uptime. Extracted and processed data from hybrid (on-premise and cloud) storage systems, integrating into Azure Data Lake to support analytics and ML initiatives. Supported ML/AI applications by preparing datasets, deploying models, and monitoring performance, ensuring high-quality inputs for predictive analytics. Built and optimized SQL Server objects (tables, views, stored procedures, indexes) to support high-performance data processing and reporting. Implemented data validation, data quality checks, and data governance practices, ensuring accuracy, reliability, and compliance across ETL pipelines. Developed interactive dashboards using Power BI and Tableau, enabling stakeholders to track KPIs and make data-driven decisions. Applied data analysis techniques including EDA, statistical modeling, and A/B testing to generate insights and support business improvements.

Designed, developed, and maintained scalable ETL pipelines using Python and SQL, enabling efficient processing and integration of large volumes of data across systems. Built and optimized data workflows using SSIS and Azure Data Factory, improving data ingestion speed and ensuring smooth transformation of raw data into usable formats. Developed and managed data warehouses and data lakes on Snowflake, Azure, and AWS, supporting business reporting, analytics, and data accessibility. Extracted and integrated data from multiple sources such as SQL Server, REST APIs, AWS S3, and Teradata, ensuring seamless data flow across platforms. Performed thorough data cleaning, transformation, and validation, maintaining high data quality and consistency for downstream analytics. Wrote complex SQL queries, joins, and stored procedures to support data extraction, reporting, and performance optimization. Automated data pipelines and reporting workflows, reducing manual effort and improving overall data processing efficiency. Monitored and troubleshot ETL processes to ensure data accuracy, reliability, and timely delivery across systems. Implemented data quality checks and validation rules to detect and resolve inconsistencies and improve data integrity. Developed interactive dashboards and reports using Power BI and Tableau, helping stakeholders gain clear and actionable insights. Applied regression analysis, A/B testing, and data mining to support data-driven business decisions. Ensured compliance with data governance, data lineage, and security standards, maintaining accuracy and reliability across data platforms. Collaborated with business, analytics, and engineering teams to understand requirements and deliver scalable and efficient data solutions.

Conducted exploratory data analysis (EDA) to identify patterns and extract actionable insights, supporting strategic initiatives in risk modelling and operational efficiency. Built 15+ interactive Tableau dashboards (Python-backed) tracking 12+ real-time KPIs, slashing manual reporting hours by 25 hours/week and improving stakeholder decisions. Built and deployed real-time anomaly detection models using Python and Scikit-learn, significantly reducing manual monitoring and enabling faster response to unusual behaviours. Developed customised credit scoring models using supervised learning techniques, improving risk assessment accuracy and enabling data-driven decision-making for client-specific verticals. Applied Natural Language Processing (NLP) to analyse customer service transcripts, extracting sentiment and intent to improve customer experience and drive an increase in satisfaction scores. Leveraged unsupervised learning techniques like clustering and dimensionality reduction to segment credit card customers, enhancing precision in marketing campaigns and product targeting. Performed model validation, hyperparameter tuning, and feature engineering to ensure statistical robustness, regulatory compliance, and performance consistency across datasets. Partnered with 7+ teams (Product, Risk, Marketing) to scope and deploy 3 model-driven solutions, cutting project turnaround time by 35% and aligning outputs with business KPIs.