Komal Shekhawat

Data Analytics and Data Engineering
• Ingested, cleaned, and structured large-scale raw datasets using Python (Pandas, NumPy), ensuring consistency,
accuracy, and alignment with internal data standards.
• Designed and implemented automated ETL pipelines for data ingestion, preprocessing, validation, and transformation
from unstructured sources into analysis-ready datasets.
• Performed exploratory data analysis, statistical validation, and anomaly detection across time-series data to identify
performance trends and deviations.
• Used SQL for data aggregation, reconciliation, accuracy checks, and analytical reporting.
• Validated analytical outputs through cross-verification with source records and logical benchmarking.
• Produced structured analytical reports, tables, and visual summaries to support technical and non-technical stakeholders.

Data Analytics, ETL Pipelines, Performance Analysis
• Led end-to-end analytical projects involving raw data ingestion, cleaning, standardisation, performance analysis, and
reporting.
• Built and maintained Python-based ETL pipelines to automate data extraction, transformation, validation checks, and
loading for analytical workflows.
• Developed and optimised 10+ Power BI dashboards and revamped 20+ existing dashboards focused on operational
performance, efficiency metrics, trends, and KPIs.
• Designed standardised analytical templates for repeatable reporting across multiple datasets and projects.
• Conducted comparative before-and-after performance analysis using statistical and trend-based techniques.
• Analysed time-series operational data to identify efficiency patterns, load trends, utilisation metrics, and performance
anomalies.
• Prepared clear technical reports with graphs, tables, and explanations to support decision-making.
• Collaborated with cross-functional teams to translate analytical findings into stakeholder-ready insights.
• Mentored junior members in Python, SQL, Power BI, ETL logic, and documentation best practices.