Ahmad F.

Responsibilities:

• Participated in setting up data pipelines from on-premises SQL Server to Snowflake, and collaborated in Agile practices such as backlog refinement and sprint planning.
• Contributed to the development of a Python Flask web application for uploading and scraping data from CSVs and spreadsheets.
• Developed and optimized large-scale data pipelines using Databricks and PySpark, managing complex ETL processes and big data workflows.

Results:

• Migrated critical data using S3 buckets and dbt, improving data accessibility and system performance.
• Delivered high-performance, scalable ETL solutions with advanced transformations and performance tuning.
• Aligned technical implementations with business objectives through proactive requirement discussions and effective team collaboration.

Responsibilities:

• Built a comprehensive Data Platform on Azure by conducting requirement-gathering sessions with various business stakeholders to support reporting and operational needs.
• Managed numerous DataOps tasks, including developing CI/CD pipelines, provisioning resources, configuring Kubernetes clusters, ingesting data, and creating scalable ETL workflows.
• Employed orchestration tools for system-to-system data transfers, maintained data models, developed Power BI dashboards, and mentored junior team members.

Results:

• Deployed robust cloud-based solutions that enhanced reporting capabilities and streamlined operational processes.
• Achieved seamless data integration and efficient transfers through optimized pipelines and effective orchestration strategies.
• Delivered actionable insights via Power BI dashboards and financial benchmarks, driving better investor decision-making and team growth.

Responsibilities:

• Collaborated with a team of 10 to build a robust data warehouse for a global retail client, using Infrastructure as Code (IaC) with Terraform to establish foundational data and analytics capabilities.
• Applied DevOps practices to automate and version-control cloud resource creation, developed Python scripts with unit tests for data extraction and loading, and utilized Azure Databricks with PySpark for complex data processing.
• Assisted in constructing dbt queries for Snowflake transformations, built migration pipelines from Salesforce to Azure using various Azure services, and provided strategic consulting on tooling and platform management.

Results:

• Delivered a scalable data warehouse solution that enhanced data integration and analytics capabilities for a global retail enterprise.
• Streamlined development workflows through automated DevOps pipelines, reducing deployment time and improving system reliability.
• Advised on cloud strategy and tooling optimization, ensuring high performance, scalability, and efficient resource utilization across client projects.

Responsibilities:

• Maintained extensive data pipelines for a data warehouse using Azure Data Factory and SQL stored procedures, ensuring seamless data flow and accurate transformations.
• Contributed to the development of a Machine Learning model by utilizing Azure Machine Learning Service and Python scripts to build and automate ML pipelines.

Results:

• Achieved reliable and efficient data integration and transformation processes, strengthening the overall data infrastructure.
• Automated ML pipeline workflows, accelerating model development and deployment and improving predictive analytics capabilities for healthcare insights.

Responsibilities:

• Developed both simple and advanced data integrations for large-scale construction projects and intra-company programs using various cloud services and programming languages on the Azure platform.
• Created data warehouses, orchestrated workflows with Azure Data Factory, Logic Apps, and Function Apps, and utilized Python packages such as Pandas for ETL processes.
• Built Power BI reports using advanced DAX functions and custom visuals to deliver insightful project analytics.

Results:

• Streamlined complex data processes and integrations, enhancing data storage, reporting efficiency, and operational performance.
• Improved data accuracy and accessibility, enabling data-driven decision-making through comprehensive Power BI dashboards.
• Delivered scalable and cost-effective cloud solutions on Microsoft Azure, ensuring consistent performance across multiple enterprise projects.