1. KPIs Management Dashboards- Performed data analytics ad-hoc.- Performed row-level security.- Analyze business KPIs and their relationships.- Collect data from the data warehouse built by the data engineering team.- Perform data transformation and enrichment using Power Query pipelines.- Create dimension data and fact data based on the Star Schema theory.- Derive insights from data and present them effectively using Power BI.- Design visually appealing dashboards in Power BI with a focus on UI/UX theory.- Ensure timely completion of projects.- Create instructional documents for users.2. Finance Data Pipeline & Use Case build up – Data Analyst/ Business Analyst- Lead a workstream to migrate customer analytical reporting to the cloud.- Collaborate with customers to understand business logic and transfer existing logic of the use case from the old system to Azure Synapse while optimizing the scripts.- Work closely with customers to understand and clarify all requirements, track progress, and ensure that all requirements are met.- Develop data pipelines to automate data ingestion and transformation to meet SLAs and reporting needs.- Adhere to coding standards and architectural designs.- Work closely with the operations team to monitor the production environment and prioritize resolving production issues, even on weekends.3. Sell In / Sell Out Automation Report (Google Looker)- Develop data pipelines to automate data ingestion and transformation by Google BigQuery.- Create Data Models and Views by LookML- Design visually appealing dashboards in Looker Dashboard with a focus on UI/UX theory.- Performed row-level security by Model in LookML.

E-commerce OptimizationResponsibilities:- Analyzed customer personas and sales performance on e-commerce websites.- Collected data using tools such as Google Analytics, Google Search Console, Google Ads, and SEMrush.- Monitored and maintained data quality and data health to ensure the data is clean, well-structured, and up-to-date.- Conducted data analysis and created insights using Google Data Studio.- Used Google Tag Manager to create website events when users interact with the website.- Detected customer journey and optimized website UI/UX.- Analyzed website user behavior.- Utilized time series prediction to predict keyword search volume on Google.- Use Time Series Prediction to predict Keyword Search Volume on Google.Technologies used:- Python (Pandas, SciPy, Re)- Google Data Studio- Google Analytics- Google Search Console.