Mansi Bhat

Collected, cleaned, and pre-processed structured and unstructured telecom data from multiple disparate sources, ensuring data quality and readiness for analysis and reporting. Performed exploratory data analysis (EDA) using Python (Pandas, NumPy) and SQL to identify trends, anomalies, and performance patterns across large-scale operational datasets. Designed and automated 15+ scalable ETL/ELT pipelines processing 500GB+ of daily telecom data, supporting ongoing analytics projects and business workflows. Built and optimised data ingestion workflows into BigQuery and Snowflake, improving data availability and usability by 30% through partitioning and clustering strategies on 2TB+ datasets. Leveraged Databricks and Apache Spark concepts for batch processing and big data transformation tasks across distributed environments. Assisted in building and validating basic predictive models and machine learning algorithms to forecast operational trends and support data-driven decision-making. Developed interactive dashboards and data visualisations (charts, graphs, KPI reports) using Power BI and Qlik Sense to communicate analytical findings to technical and non-technical stakeholders. Explored and supported the application of AI-based and cognitive automation solutions to manage data pipelines, predict scenarios, and prescribe operational actions. Ensured data privacy and security standards were maintained throughout all data collection, transformation, and reporting processes in compliance with organisational policies. Documented data sources, methodologies, transformation logic, and analytical processes to ensure transparency, reproducibility, and knowledge transfer. Collaborated cross-functionally with business analysts, IT teams, and data engineers to translate project requirements into actionable data insights and summary reports. Implemented partitioning and clustering on 2TB+ BigQuery datasets, reducing query time by 35% and cutting cloud costs by 18%.