Here's not just what I use, but how I apply it:
📊Data Analysis & BI - SQL (Advanced): I’ve written and optimized complex, multi-join queries handling millions of banking transactions — cutting retrieval time by 25% and powering real-time dashboards. - Python: For statistical modeling, A/B testing, data cleaning, and building validation pipelines. Libraries include Pandas & NumPy/br> - Power BI & Tableau: Built multi-layered dashboards that integrate API data, Oracle databases, and key KPIs — used by executives for compliance, risk, and operations. - Microsoft Excel & R: For rapid prototyping, statistical models, and cross-verification of results.
🔁ETL, APIs & Data Engineering - MuleSoft & Anypoint Studio: Extracted and transformed raw data from APIs into analysis-ready tables. Streamlined ETL workflows that reduced manual effort by 30%. - Oracle & MongoDB: Managed both relational and NoSQL environments. Understood trade-offs in schema design, indexing, and query performance. - AWS, Git, BitBucket, SharePoint: For version control, cloud integration, and collaborative workflow management.
📈Tools & Agile Methodologies - JIRA, Confluence, Salesforce: Managed analytics tasks in sprint cycles, gathered requirements, and translated business needs into technical blueprints. - SAP, Agile, Scrum: Familiar with ERP workflows and iterative development models that drive velocity without sacrificing quality.