Aman Mathur

Data Engineer with dual internship experience building production-grade metadata-driven ETL pipelines on Azure Data Factory and Databricks. Hands-on expertise across the Azure data stack (ADF, Databricks, Delta Lake, PySpark) and cloud platforms (AWS, Snowflake), with a focus on medallion architecture, CDC, and centralised metadata control.

Data Engineer Intern at DATANSH SOLUTIONS (2026-04 – 2026-06)

• Designed a metadata-driven Azure Data Factory pipeline ingesting data from on-premise SQL Server across multiple databases, driven by a single centralised metadata table storing source/target mappings, load types, watermark columns, and execution order.
• Engineered T-SQL stored procedures supporting both watermark-based incremental and full-load strategies, with all switching logic controlled dynamically through the metadata table — eliminating hardcoded table-specific pipeline logic.
• Built a dedicated log table to capture pipeline execution status, row counts, and failure details, enabling rapid diagnosis and resolution of data inconsistencies across staging and warehouse layers.

Data Engineer Intern at REGEX SOFTWARE SERVICES (2025-09 – 2026-03)

• Built parallelised Python and SQL ETL pipelines for multi-source data ingestion, reducing end-to-end processing time by 30% through concurrent execution and query-level tuning.
• Developed a data validation framework to detect schema anomalies across 3+ production dashboards, improving overall data reliability scores.
• Improved SQL query performance by 25% through targeted indexing strategies and execution plan analysis across transactional and reporting workloads.

Bachelor of Technology in Computer Science Engineering – University of Technology (2021 – 2025)