Islamabad, PK
Splenic Manifestor 5/1 : INFP-T

Experience

Nayatel

Nayatel // Full-time

Principal Data Engineer

Jan 2025 - Jun 2026 | Islamabad, Pakistan | On-site

Led the modernization of telecom analytics pipelines by moving legacy ETL into a cloud-native Snowflake and AWS stack, with a focus on reliability, modularity, performance, and faster decision-making for service intelligence.

What I owned

  • Migrated legacy ETL workloads to AWS-native architectures using Amazon S3, Snowpipe, and Snowflake for scalable analytical processing.
  • Built reusable dbt transformation layers with Jinja macros, schema tests, and incremental models to improve maintainability and trust.
  • Designed warehouse models for telecom service intelligence, customer lifecycle analytics, and churn reporting with dimensional modeling.
  • Orchestrated production workflows with Apache Airflow on AWS MWAA, handling retries, dependencies, and production reliability.
  • Optimized Snowflake performance using clustering, incremental loading strategies, and warehouse tuning to reduce latency and compute cost.
  • Integrated Snowpark and Cortex AI capabilities to modernize Python-heavy workflows and enable AI-assisted analytics directly in Snowflake.
Tech Stack Amazon S3 Snowpipe Snowflake dbt AWS MWAA Lambda IAM
PLC Group

PLC Group // Full-time

Data Engineer

Aug 2022 - Jan 2025 | Islamabad, Pakistan | Hybrid

Built large-scale data platforms for telecom analytics, combining batch and streaming systems across AWS to move operational signals from source systems into governed lakehouse and warehouse layers.

What I owned

  • Developed large-scale ETL pipelines with AWS Glue and PySpark for telemetry and operational data from 2,500+ telecom sites.
  • Architected event-driven ingestion using Amazon MSK, Kinesis Data Streams, and Kinesis Firehose for near real-time lake ingestion.
  • Implemented Medallion Architecture across raw, refined, and curated layers to improve traceability and downstream analytical consistency.
  • Used AWS DMS for reliable source-to-cloud migration from relational systems into analytical environments with lower sync latency.
  • Optimized Redshift and Snowflake workloads through partitioning, clustering, query tuning, and efficient transformation design.
  • Provisioned reproducible infrastructure with Terraform across IAM, S3, EC2, and orchestration environments.
Tech Stack Python AWS Glue PySpark Kafka Kinesis Terraform Redshift Snowflake
Quixel

Quixel // Internship

Machine Learning Intern

May 2022 - Oct 2022 | Islamabad, Pakistan | Hybrid

Worked at the intersection of computer vision and creative tooling, building automation and ML-assisted workflows that connected Photoshop scripting with image-processing and synthetic-data tasks.

What I owned

  • Developed and evaluated machine learning workflows in Python for computer vision and image analysis use cases.
  • Integrated ML workflows into Adobe Photoshop through JavaScript-based automation and custom scripting.
  • Built scripting tools for material segmentation and image inpainting to streamline annotation and editing pipelines.
  • Developed synthetic-data generation tooling in Photoshop to support training data creation and augmentation.
  • Collaborated on image-processing and workflow-automation tasks that improved experimentation speed for visual ML work.
Tech Stack Deep learning Adobe Photoshop Python JavaScript Computer vision
CAST

CAST // Internship

Embedded Engineer Intern

Jan 2022 - Mar 2022 | Islamabad, Pakistan | On-site

Explored embedded and applied vision systems using Raspberry Pi, OpenCV, and classical ML workflows, with research centered on practical experimentation and agricultural image-analysis tasks.

What I owned

  • Researched, configured, and deployed Raspberry Pi 4 environments for embedded and computer vision experimentation.
  • Worked with OpenCV and Python on image acquisition, preprocessing, feature extraction, and recognition workflows.
  • Investigated machine learning approaches for agricultural quality assessment and seed-classification problems.
  • Collaborated with supervisors and researchers to evaluate model behavior and improve classification performance through iterative testing.
Tech Stack Embedded systems Python MATLAB OpenCV Machine learning