Rameez Raz Afzal Mohammed

About Rameez Raz Afzal Mohammed

With ~6 years of hands-on experience in data engineering and data migration, I specialize in designing and developing scalable, secure, and efficient data pipelines across cloud platforms like Azure and AWS. I’ve led and supported complex migrations from legacy systems to the cloud, enabling analytics-ready data environments for enterprise reporting and insights. What I Do:• Build robust ETL/ELT pipelines using Azure Data Factory, Databricks, Synapse Analytics, and Azure SQL• Work with AWS S3, Athena, and Glue to handle large-scale datasets and optimize performance• Support end-to-end data integration, transformation, and migration projects• Ensure data accuracy, security, and availability for analytics and reporting teams• Collaborate cross-functionally to solve business problems using data-driven solutions Technical Proficiency:• Big Data & Processing: Hadoop, Spark, PySpark, Hive, HBase• Cloud: Azure (ADF, Synapse, Databricks, Blob Storage, Cosmos DB, Microsoft Fabric), AWS (S3, Glue, Athena)• ETL Tools: Informatica, Ab Initio• Programming: Python, Java, SQL, Shell Scripting• Databases: Oracle, MySQL, Teradata, Snowflake, Hive• Scheduling/Workflow: Airflow, Control-M• Visualization: Power BI, Tableau• Collaboration Tools: JIRA, Confluence, ServiceNow, SharePoint Let’s Connect:I’m passionate about leveraging cloud and data technologies to deliver business impact and continuously improve data systems. Open to new opportunities—onsite or remote. Always eager to learn new tools and contribute to meaningful data-driven projects.

Experience

1. Associate

   Cognizant

   Feb 2022 — Present · Hyderabad, IN

   Migrated datasets from Hadoop HDFS to AWS S3, using PySpark to transform and partition data into optimized Parquet format, improving query performance and reducing storage costs by 35%.• Automated ETL pipelines with PySpark to process financial and occasionally sensitive personal data, catalogued using AWS Glue and validated with Athena.• Led migration of large-scale financial data from Teradata to Snowflake, leveraging external stages for automated ingestion from S3 and enabling timely data availability for reporting.• Performed data validation and reconciliation using ABI MTRS, Fermi, control totals, and hash checks, ensuring <0.5% variance and adherence to compliance standards.• Implemented security controls, including Snowflake view-level filters, data masking, and role-based access, alongside IAM roles and S3 bucket policies to protect sensitive data.• Scheduled and monitored ETL workflows using Control-M, with Splunk logs for tracking AWS and Snowflake pipelines, reducing failure rates by 40% through proactive alerting and debugging.• Maintained ETL code repositories in Bitbucket with peer-reviewed workflows; managed dataset versioning through the Swagger Portal.• Used Jules for deployment automation, and built containerized ETL components using Docker, publishing images via JFrog Artifactory.• Leveraged Postman for API testing and automation within data ingestion and validation workflows.• Handled change requests and incident resolution through ServiceNow, supporting traceability, governance, and audit readiness.

Education

• Kakatiya University

  Bachelor of Technology, Information Technology

  2015 — 2019