Atif Imam

● Architected end-to-end ELT workflows using Airflow, AWS, Snowflake, and DBT, enabling modular data modeling for complex datasets. ● Reduced monthly cloud compute costs by 30% by optimizing Snowflake warehouse utilization and query performance. ● Improved data reliability for stakeholders by engineering a comprehensive data quality framework with schema enforcement and reconciliation logic. ● Architected, designed and implemented tag based masking policies for the business facing views and tables

● Optimized GCP data pipelines, resulting in reduced total processing time and significantly increased system reliability for high-volume customer data. ● Developed complex analytical models to measure email/SMS campaign effectiveness, directly enabling the marketing team to reduce customer fatigue and optimize communication frequency. ● Designed and maintained fault-tolerant Airflow DAGs, ensuring seamless automation of business-critical data workflows.

● Extracting Data from different data sources like RDS (Mysql, Postgres) BigQuery, and third-party APIs. ● Parsing/Extracting CSV, JSON files and automating the extraction using python scripts. ● Transforming the raw data and loading it into target databases like redshift and snowflake. ● Performing quality checks for the data to make sure the data is clean and usable. ● Orchestrating the D.E jobs to automate the extraction and loading of the data. ● Managing data analysis and processing activities involving analyzing, studying, and summarizing data for extracting useful information which would assist in strategic decision-making and planning

● Achieved a 10x reduction in Parquet file ingestion runtime through performance engineering and optimized Spark logic. ● Served as a core team member for the flagship product "DataMax," building complex business logic in PySpark, Redshift, and Matillion. ● Developed a custom Data Quality Check pipeline using Dagster to ensure high accuracy across all data deliverables.

● Developed PySpark and Python scripts to automate manual data tasks, significantly increasing team productivity. ● Converted legacy Hive queries into PySpark scripts, reducing runtime and infrastructure resource consumption. ● Built the logic for processing and populating tables for pricing and sales data sourced from SAP systems. ● Contributed to a seamless data loading framework that improved data accessibility for all downstream users.

● Led the migration of legacy PostgreSQL pipelines from Airflow to Databricks, resulting in enhanced performance and streamlined resource utilization. ● Enhanced data pipelines to boost efficiency and reduce processing times by leveraging Databricks and PySpark. ● Refined complex SQL queries and workflows to minimize operational overhead and further reduce infrastructure costs.