Wasifuddin Muhammed

Data Engineer

Talent

HyderabadMember since July 17, 2026

PythonSQLGCPBigQueryCloud ComposerDataformApache AirflowBatch ProcessingDimensional ModelingSCD Type 2Surrogate Key GenerationParquetHot-to-Cold Storage TieringData Quality ValidationAlertingSLA Monitoring

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PythonSQLGCPBigQueryCloud Composer

About

Data Engineer with production experience designing cloud-native data pipelines, automated archival systems, and DAG-based workflow orchestration on GCP using Python, SQL, Apache Airflow, Dataform, and BigQuery. Built and delivered a production archival pipeline processing ∼100M rows daily that reduced warehouse storage costs by 40–50% through idempotent batch processing, automated data quality validation, self-healing recovery, and structured operational observability. Contributed SQL logic and dimensional modeling inputs to Dataform ELT workflows incorporating SCD Type 2 change tracking, surrogate key generation, and structured audit logging.

Experience

Associate Data Engineer - Fariz Infosolutions Pvt. Ltd. (FISClouds) - Hyderabad, India

(2025-01 - 2026-06)

Designed and maintained a production-grade, idempotent Airflow DAG (Cloud Composer) identifying near-expiry BigQuery partitions and archiving them as ZSTD-compressed Parquet files to GCS — processing ∼100M rows daily and reducing warehouse storage costs by 40–50% through automated hot-to-cold storage tiering.
Implemented adaptive SLA monitoring using historical runtime patterns to dynamically classify and alert on pipeline delays, replacing static manual thresholds with data-driven observability.
Built a three-stage data quality framework verifying row counts, column counts, and schema consistency before marking partition exports as successful.
Developed a self-healing, idempotent recovery workflow that automatically identifies and reruns only failed or missing partitions, ensuring full data retention compliance without manual intervention.
Parallelized partition purge verification across ∼150 tables, reducing execution time by ∼98% vs. sequential processing.
Contributed SQL logic and dimensional modeling inputs to Dataform ELT pipelines: SCD Type 2 change tracking, surrogate key generation, effective-date management, and job-level audit logging.
Designed monitoring workflows separating pipeline-level execution failures from data-quality failures, enabling precise and actionable operational alerting.