Aaditi Pajankar

Developed ETL pipelines for data ingestion and transformation using Azure Data factory and Databricks, processing 50 GB+ of data daily. Assisted a senior team member on a Python-based project, gaining exposure to scripting and understanding basic data processing workflows. Boosted azure pipelines performance by 30% using broadcast joins and handling data skewness. Worked on multiple azure data pipelines to ingest data in reporting tables. Built optimized data models (star schema) to enhance query performance by 30%. Handled complex use cases and delivered solutions that catered to multiple requirements in a single dashboard. Utilized Microstrategy as a BI tool to deliver project which involved schema design, security, data modeling and dashboarding.

4.5+ years of industry experience providing problem-solving abilities in data analytics and business intelligence domain. Accountable for translating business needs into technological specifications. Delivered end to end solution in BI and ETL involving DBI’s, MSTR Schema architecture, data pipelines, databases, data warehouse, SQL, ingestion process, AWS S3. Excellent leadership skills with critical thinking abilities and capacity to handle two client-facing projects. Designed 10+ BI dashboards in multiple BI tools that extracts data from various data sources. Worked on writing complex SQL queries to extract and analyse data, and contributed to the development of new performance metrics, providing insights to enhance business decision-making. Tested 100+ critical documents and 50+ non-critical ones, identified the impact on 50+ subscriptions, and altered 15+ cubes to address issues in data migration project. Collaboration with cross-functional teams to manage data requirements and ensure seamless customer experience. Developed BI solutions to process and analyse large-scale, complex datasets, improving reporting and decision-making. Performance Tuning, including changes to the sql queries and creating in-memory cubes were carried out which resulted in a 50% decrease in the execution time of the dashboard.