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Ahmad Waleed HamidAH
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Ahmad Waleed Hamid

@ahmadwaleedhamid

Lead Data Engineer building real-time, cloud-native data platforms on AWS to deliver low-latency analytics.

Pakistan
Message

What I'm looking for

I’m looking to lead scalable AWS data platforms in fast-paced environments—building real-time pipelines, improving performance/latency, and shipping reliable ETL/ELT workflows with strong governance and monitoring.

I’m a Lead Data Engineer and founder who builds and scales end-to-end data platforms from scratch on AWS, with a focus on real-time financial processing and low-latency analytics.

I design real-time data pipelines and high-throughput ingestion systems, modeling NoSQL/SQL data for massive volumes and optimizing performance by up to 40%. I’ve implemented Elasticsearch for sub-second query response times and improved query latency through MongoDB modeling and aggregation pipeline tuning.

Across ETL/ELT and orchestration, I’ve delivered scalable ingestion and transformation workflows using Python, Talend, and Apache Airflow (Dockerized), while adding validation, governance, and monitoring to reach 99%+ data accuracy. I also lead engineering execution—leading a team of 4 engineers—and partner with stakeholders to ship data-driven APIs and dashboards that support production systems.

Experience

Work history, roles, and key accomplishments

AF

Associate Data Engineer

Afiniti

Mar 2021 - Feb 2024 (2 years 11 months)

Delivered scalable ETL pipelines with Talend processing 500M+ records/month across real-time and batch datasets. Migrated legacy workflows to Dockerized Apache Airflow, reducing job failures by 30%, while improving SQL performance by up to 40% and automating reporting to save 70+ hours/month.

AO

Data Integration Officer

Allied Bank (Head Office)

Nov 2019 - Mar 2021 (1 year 4 months)

Maintained ETL pipelines handling 15M+ records/day with 99.99% availability and enabled WhatsApp banking analytics for 10M+ customers. Supported Hadoop modernization with IBM and built real-time processing workflows using Spark, Hive, and MapReduce, while automating SOA/e-SOA pipelines saving ~500 man-hours/year.

Education

Degrees, certifications, and relevant coursework

FAST - National University of Computer and Emerging Sciences logoFS

FAST - National University of Computer and Emerging Sciences

Bachelor of Science, Computer Science

2019 -

Activities and societies: Fall Detection Using Kinect 2.0 (C#, Python, Android Studio); Nash Equilibrium Simulation (Game Theory); IRIS Recognition System (Python, MATLAB).

Pursued a BS in Computer Science at FAST, focusing on applied engineering projects. Built systems including Kinect-based fall detection, a Nash equilibrium simulation using game theory, and an AI-based iris recognition/segmentation system using Python and MATLAB.

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