Building cloud-scale data platforms, publishing peer reviewed AI research, and authoring expert commentary to drive data-informed innovation.
I design and build reliable, scalable data systems. From ingestion to orchestration, I work across the stack to ensure data flows efficiently and remains readily available. My experience spans cloud pipelines, distributed processing, and operational data warehousing. I optimize for performance, maintainability, and cost without compromising quality.
I develop machine learning models that go beyond experimentation. My work focuses on end-to-end ML workflows,from feature engineering and training to evaluation and deployment. I prioritize interpretability, scalability, and real-world impact, with a strong grounding in statistical thinking and algorithmic design.
I translate complex data into clear, actionable insights. Whether it's building interactive dashboards or performing exploratory analysis for on-demand inquiries, I help teams make informed, data-backed decisions. I focus on metrics that matter, and communication and reporting that ensure stakeholder engagement.
I combine a strong academic foundation with practical AI innovation, grounded in peer-reviewed research on optimization, ensemble learning, healthcare AI e.t.c. My research anchors every project in rigorous evidence. I translate complex technical advancements into accessible insights and contribute to ethical, human-centered AI conversations in leading publications.
Spearheaded and maintained data pipelines and warehouse solutions to support infrastructure planning, leveraging Azure Synapse Analytics, Azure Data Factory, SSIS, and Python.
Developed and deployed machine learning models to deliver insights and support data-driven decisions across industries.
Defined product strategy, managed prioritized backlogs and requirements, and drove value through cross-functional stakeholder collaboration.
Delivered actionable insights using interactive Power BI dashboards and optimized key metrics to support strategic decisions.
Utilised Python to build predictive models and extract actionable insights, optimizing logistics operations.
Leveraged Python and ML to forecast demand and optimize corporate travel operations.
Accurately entered and maintained financial data using Excel and accounting software, ensuring data integrity and confidentiality.
Trained in advanced Big Data systems including machine learning, data mining, and cloud technologies like Hadoop and Spark.
Specialized in algorithms, software engineering, artificial intelligence, and data analytics.
Professional development in IT strategy alignment and technology-driven transformation.
Focused on high-level decision-making, risk assessment, and management in complex organizations.
Focused on digital systems design, microprocessor architecture, and embedded systems.
Designed and optimized a scalable Data Lake and SQL Pools, building high-performance ETL pipelines while troubleshooting and resolving failures through root-cause analysis. Automated data quality validation across multiple layers using pytest, significantly improving reliability and reducing pipeline downtime. Implemented event-driven architectures with Azure Function Apps and Service Bus, integrating Databricks notebooks for fault-tolerant workflows. These optimizations minimized latency, reduced manual intervention, and strengthened end-to-end data resilience.
Designed and deployed reusable, modular SSIS and Azure Data Factory (ADF) pipelines using Python scripting. Automated scheduling, error handling, and logging to improve reliability, reducing manual intervention by 60%. Migrated legacy SSIS workflows to cloud-native ADF, optimizing performance and scalability. Implemented data validation checks, increasing accuracy by 75% and cutting maintenance overhead by 40%.
Designed and implemented a scalable data integration solution by consolidating 25+ disparate data sources through optimized SQL stored procedures and ETL pipelines. Established real-time cross-database synchronization using change data capture (CDC) and message queueing, reducing query latency by 60% and accelerating analytics delivery by 3x.
Designed and implemented CI/CD pipelines for Azure Data Factory (ADF) using Azure DevOps, integrating Git version control for source-controlled development and rollback capabilities. Automated testing and deployment workflows reduced integration failures by 40% and accelerated release cycles by 50%, enabling faster, more reliable data pipeline deployments.
Automated sales reporting by migrating on-premises SQL databases to Azure, transforming and processing data in Databricks, and developing an interactive Power BI dashboard to visualize daily/monthly sales trends and top-performing products. This eliminated manual reporting, providing real-time KPIs to optimize inventory planning and enhance promotional targeting.
Conducted sentiment and topic analysis on web-scraped airline customer reviews, then trained a Random Forest model to predict booking success. Generated visualizations including word clouds and feature importance plots to identify key sentiment drivers. Insights derived from the analysis informed targeted marketing strategies, leading to improved booking conversion rates.
Loaded and processed security logs in Apache Spark, performing exploratory data analysis (EDA) to identify attack patterns and trends. Developed a Linear Regression model to predict network attack frequency, leveraging Seaborn for visualization to derive actionable insights. Implemented predictive security monitoring, reducing incident response times through proactive threat detection.
Processed the UNSW-NB15 network dataset using PySpark for scalable data handling, applied Bisecting K-Means clustering to segment traffic patterns, and trained a Random Forest classifier to identify attack signatures. This approach enhanced threat detection accuracy and reduced latency, enabling real-time, proactive security responses.
Integrated HR and absenteeism datasets using SQL, employing optimized JOINs and WHERE clauses to filter employees with low absenteeism. Designed an interactive Power BI dashboard featuring demographic trends, absence patterns, and NLP-generated insights from employee feedback. Spearheaded a data-driven bonus initiative, targeting high-performing, low-absence staff, resulting in a 65% reduction in absenteeism and improved wellness program ROI.
TechBullion | Tech News
Associated Press News
International Journal of Scientist Research and Modern Technology
2024 IEEE 12th International Conference on Intelligent Systems (IS)
World Journal of Advanced Engineering Technology and Sciences
World Journal of Advanced Research and Reviews
The Guardian | News
International Journal of Innovative Research and Development
International Journal For Research & Development In Technology
