Azeez Bhai

B.Tech Bioinformatics student with hands-on experience building data engineering projects using Python, SQL and Azure. I have worked on metadata-driven ETL pipelines using Azure Data Factory, hybrid ingestion from PostgreSQL and Azure SQL into ADLS Gen2, and incremental data loading using watermark-based processing. I have also gained practical experience with data extraction, Pandas and Power BI through my data engineering internship.

Data Engineering Intern at Perzonix Technologies (2026-06 – 2026-08)

Hybrid Metadata-Driven Cloud Ingestion Framework | Azure Data Factory

• Architected a single reusable, metadata-driven ADF pipeline (Lookup, ForEach, If Condition, Copy Data, Stored Procedure) ingesting 1M+ records from on-premises PostgreSQL and Azure SQL Database into ADLS Gen2 as partitioned Parquet, enabling zero-code onboarding of new tables via metadata insert.
• Implemented hybrid connectivity via Self-Hosted Integration Runtime (SHIR) for on-prem PostgreSQL and Azure AutoResolve IR for Azure SQL, decoupling cloud orchestration from on-prem network dependencies.
• Built watermark-based incremental loading (control tables + stored procedure bookmarking) to avoid full reloads on every run, with fully parameterized Linked Services, Datasets, and dynamic sink paths for schema/table-level reuse.
• Converted landing zone from flat-file to Parquet with date-based partitioning, aligning with Medallion Architecture (Bronze layer) and optimizing for downstream Spark/Databricks consumption.
• Secured all credentials via Azure Key Vault; maintained isolated DEV/TEST/PROD environments with environment-specific Linked Services.
• Filtered the metadata Lookup query (WHERE is_active = 1) to process only active tables per run, avoiding unnecessary iterations and reducing pipeline execution cost.

B.Tech in Bioinformatics – Vignan University

Intermediate – State Board