M. Abdullah Bin Aftab

Experience

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  KI Technologies (Pvt.) Ltd.

  Sept 2022 - Mar 2023

  • Digital Marketer(ASO, SEO)

    Nov 2022 - Mar 2023

  • Junior Digital Marketer (ASO, SEO)

    Sept 2022 - Nov 2022
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  Bytewise Limited

  Mar 2023 - Jun 2023

  Data Engineer

  - Developed and automated end-to-end ETL pipelines using Azure Data Factory, integrating multiple data sources into Azure Data Lake Storage Gen2.- Implemented Databricks workflows to process large datasets, optimizing transformations using PySpark and SQL.- Designed a Delta Lake architecture, ensuring efficient data versioning, ACID transactions, and query performance.- Secured sensitive credentials and storage account keys using Azure Key Vault, enabling seamless authentication for Databricks.- Improved data reliability by implementing monitoring and logging mechanisms to track pipeline performance and failures.- Optimized SQL queries and PySpark scripts to improve processing efficiency and reduce compute costs. Show less

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  MetaSol

  Sept 2024 - Apr 2025

  Associate Data Engineer

  1. Designed and implemented scalable ETL pipelines using AWS Glue and Azure Data Factory, enabling seamless data integration and transformation across cloud platforms to support data-driven decision-making.2. Automated data validation and integrity checks through AWS Lambda and Azure Logic Apps, ensuring high data accuracy (99.7% validation rate) while improving overall data quality and compliance with business rules.3. Collaborated with cross-functional teams to understand business requirements and deliver actionable insights, leveraging AWS S3, Redshift, and Azure Data Lake for optimized data storage and processing.4. Built cloud-based data architectures using AWS and Azure services such as EC2, Glue, Lambda, and Azure Synapse Analytics, facilitating real-time and batch data processing for enhanced business intelligence capabilities.5. Optimized data processing workflows using AWS Kinesis and Azure Stream Analytics, enabling real-time data analytics to support critical business operations.6. Developed and maintained cloud-based data pipelines for large-scale data transformation, utilizing AWS S3 for storage, Glue for processing, and Redshift for analytics, ensuring data availability and scalability for business applications.7. Managed data integration using AWS Redshift, Snowflake, and Azure SQL Data Warehouse, enabling seamless data loading, transformation, and analysis for business stakeholders.8. Led data governance initiatives to ensure data security and integrity across platforms, utilizing AWS IAM, Azure Active Directory, and best practices in cloud security.9. Developed end-to-end data workflows for ingesting, transforming, and analyzing structured and unstructured data, leveraging Apache Spark, Databricks, and PySpark for distributed computing and high-performance data processing.10. Implemented data lakehouse architecture using AWS Lake Formation, Azure Data Lake Storage, and Snowflake, enabling scalable data management and real-time analytics. Show less

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  Decision Inc. Australia

  Mar 2025 - now

  Data Extraction Engineer

  The role focused solely on Azure, incorporating Azure Data Factory, Databricks (PySpark transformations), and SQL Queries, along with a project in one of a wood window and door manufacturing company in the United States1. Designed and implemented scalable ETL pipelines using Azure Data Factory, enabling seamless data extraction, transformation, and integration to support data-driven decision-making for a wood window and door manufacturing company in the United States.2. Developed and optimized PySpark transformations in Azure Databricks, ensuring efficient data processing and transformation for large-scale datasets.3. Automated data validation and integrity checks using Azure Data Factory and Azure Functions, ensuring a 99.7% validation rate while maintaining high data accuracy and compliance with business rules.4. Collaborated with cross-functional teams to understand business requirements and deliver actionable insights, leveraging Azure Data Lake Storage (ADLS) for optimized data storage and processing.5. Built cloud-based data architectures using Azure Synapse Analytics, Azure Data Lake, and Databricks, facilitating real-time and batch data processing to enhance business intelligence capabilities.6. Optimized SQL queries for performance tuning in Azure SQL Database ensuring fast and efficient data retrieval for reporting and analysis.7. Managed data integration and transformation workflows across Azure Data Lake and Databricks, enabling seamless data loading, transformation, and analysis for business stakeholders.8. Developed end-to-end data pipelines for ingesting, transforming, and analyzing structured and unstructured data, leveraging Apache Spark and PySpark in Databricks for distributed computing and high-performance processing. Show less