Ammar Amjad

As a Software Engineer at Apple, I am part of the POS Engineering team, developing and optimizing database solutions to enhance the retail experience for employees and customers globally. This role involves high-impact projects that uphold Apple’s commitment to exceptional user experiences.

I collaborate with cross-functional teams to design scalable, high-performance database architectures using technologies like Oracle PL/SQL, Docker, and Kubernetes. My focus is on creating reliable solutions that support millions of customers worldwide.

This role has sharpened my skills in database design, microservices, and cloud technologies. I also play a key role in resolving production issues and ensuring system efficiency, contributing to transformative advancements and my growth as an engineer.

I am currently pursuing a Master of Science in Computer Science at the University of Florida (UF). The university offers a comprehensive program that provides in-depth knowledge and advanced coursework in various areas of computer science.

Engaging with esteemed faculty members has been invaluable, fostering my academic and professional development. Participating in cutting-edge research projects in data analysis, machine learning, and artificial intelligence has expanded my knowledge and skills.

Overall, UF has provided an enriching experience, equipping me with a strong foundation for a successful career in computer science.

The Certified Associate Cloud Engineer certification validates the ability to deploy applications, monitor operations, and maintain GCP projects. I possess in-depth knowledge of essential GCP services, including computing, storage, networking, databases, and security.

The key features are below:

• Deploying applications on Compute Engine, Kubernetes Engine, and App Engine, ensuring optimal performance and scalability.
• Designing secure and high-performance network architectures using GCP's Virtual Private Cloud (VPC), Cloud Load Balancing, Cloud DNS, and Cloud CDN.
• Implementing best practices in identity and access management for data security.
• Utilizing GCP's monitoring and logging tools to proactively monitor application performance and optimize resource utilization.
• Configuring and managing networking components within GCP to ensure optimal performance and connectivity.

Worked with software, cloud, and database development, focusing on ETL pipelines for enterprise applications. Ensured successful A/B test cases through data validation, cleaning, and testing. Contributed to the migration, performance tuning, and automation of systems.

• Developed ETL pipelines for IBM's Global Asset and Configuration Management Data Warehouse, reducing lookup and search time by over 50%.
• Contributed to data automation and system administration, utilizing shell scripting and Linux/Unix environment.
• Supported the migration of Teradata and Netezza to DB2, optimizing SQL queries and stored procedures.
• Researched and presented proof of concept for ELK stack use case for Inv360 project
• Implemented ETL operations, comparison logic and data visualization for Inv360 project.
• Enhanced ETL system responsiveness using DataStage, Excel, and Cognos.
• Supported end-to-end design, implementation, and maintenance of data warehousing and integration solutions.

• Python
• C++
• AWS
• GCP
• ELK Stack
• Datastage

The Microsoft Certified Azure Data Fundamentals certification validates my expertise in foundational concepts of data and analytics on the Azure platform. After passing the DP-900 exam, I have gained a deep understanding of Azure data services and their capabilities.

Key highlights of this certification are as follows:

• Acquiring knowledge of core data concepts, relational and non-relational data, data storage options, and data processing technologies on Azure.
• Understanding the basics of data governance, data privacy, and data compliance within the Azure environment.
• Gaining insights into various Azure data services, such as Azure Cosmos DB, Azure SQL Database, Azure Data Lake Storage, and Azure Databricks.
• Exploring data integration and migration scenarios using Azure Data Factory and Azure Synapse Analytics.
• Utilizing Azure Machine Learning to implement machine learning models and gain predictive insights from data.

During my time at VisionX, I played a crucial role in supporting enterprises, including WeWork and EMAR, to achieve significant cost savings through the design and enhancement of an inventory management application for global logistics and delivery operations.

Key accomplishments and responsibilities include:

• Improved data usability by conducting data parsing and converting it into a backend-compatible format using Python and pandas.
• Facilitated analytics by performing data cleansing, formatting, and transformation of data in CVS files and pandas data frames.
• Discerned key insights on the package delivery process by devising a dashboard and evaluating data from the MSSQL database using Redash.

I completed my Bachelor of Engineering in Computer Engineering from the National University of Sciences & Technology (NUST), Pakistan.

During my time at NUST, I gained a strong theoretical foundation and practical skills in computer engineering. The rigorous academic environment and quality faculty helped me develop a deep understanding of core concepts and cutting-edge technologies.

Being an alumnus of NUST, I take pride in the education I received and the values instilled in me during my time there. The knowledge and skills I gained from NUST have been instrumental in shaping my career as a Data Engineer and Data Scientist.

During my freelance work, I implemented a high-frequency cryptocurrency trading bot utilizing news sentiment analysis and technical indicators.

Key highlights of the project include:

• Determined and assessed detection trends in the cryptocurrency market by using technical indicators like SMA-fast and SMA-slow with a variable range.
• Designed and developed a bot that analyzed news sentiments and technical indicator flags for fully and semi-automated cryptocurrency trading.
• Acquired pertinent news data using the Google RSS feed, enabling the bot to make informed trading decisions based on real-time information.
• Achieved an impressive sentiment detection and language modeling accuracy of over 92% through robust algorithms and machine learning techniques.

If you would like to discuss potential collaborations, have any questions, or simply want to connect, feel free to reach out to me. I am always open to new opportunities and conversations.