Ibtasam Amjad

Aspiring Data Engineer with a strong foundation in SQL, Python, PySpark, and data analysis, supported by hands-on experience working with Databricks, Window Functions, data cleaning, and ETL-style operations. Adept at analyzing large datasets, automating tasks using Python, and applying analytical thinking developed through cybersecurity and technical research roles.

Data Engineer at Tiers Limited (2025-11 – Present)

Data Engineer role focused on ETL/ELT pipeline development and data infrastructure management

• Designed, built, and maintained end-to-end ETL/ELT pipelines using Python and SQL
• Processed and transformed large-scale datasets using Apache Spark and Databricks
• Developed and optimized data warehouse schemas (fact and dimension tables) for analytics and reporting
• Implemented data quality checks, validation rules, and error handling to ensure data reliability
• Optimized pipeline performance through query tuning, partitioning, and Spark job optimization
• Automated data ingestion from databases, APIs, and flat files using scheduled workflows on AWS (S3, Glue, Lambda)
• Built and maintained data infrastructure on AWS, leveraging S3 for storage, Glue for ETL orchestration, and snowflake for data warehousing

Data Analyst at Pligence Inc (2024-09 – 2025-11)

Data Analyst role focused on data analysis, pattern recognition, and pipeline development

• Performed data analysis and classification by mapping behavioral patterns and events to structured analytical frameworks to identify trends and anomalies
• Conducted in-depth research and analysis on emerging data patterns, ensuring insights aligned with evolving business and technology trends
• Built and maintained datasets from multiple structured and unstructured sources, enabling accurate reporting and decision-making
• Collected, validated, and standardized key indicators and metrics through detailed exploratory data analysis (EDA)
• Designed and optimized rule-based logic and pattern-matching techniques to support data quality and analytical workflows
• Performed data curation and cleansing, identifying and eliminating false positives, inconsistencies, and noise to improve dataset reliability
• Automated repetitive analytical tasks using Python scripting and regular expressions, improving efficiency and reducing manual effort
• Built and optimized data pipelines for structured and semi-structured datasets to support analytics and reporting

BS in Computer Science – University of Engineering and Technology (2020-09 – 2024-06)