Ayesha Siddiqua

I’m a Software and Data Engineer with 3+ years of experience building scalable data platforms and cloud-native systems for analytics and real-time applications. I enjoy turning high-volume, messy data into reliable infrastructure—end-to-end pipelines, backend APIs, and machine learning workflows.

At Missouri University of Science and Technology, I architected and implemented scalable data processing pipelines for the Missouri Water Information System, integrating rainfall, drought, and IoT sensor APIs into a centralized PostgreSQL platform supporting 10M+ time-series records. I designed relational schemas and time-series data models that reduced query execution time by 30%, built Python ETL pipelines with automated validation and anomaly threshold checks, and improved analytical query performance by 35%+ using optimized SQL and indexing. I also implemented incremental processing to cut full refreshes and lower pipeline execution time by 40%, while building Plotly/Dash monitoring dashboards for 200+ researchers.

Previously, as a Software Engineer (Remote) at Swish Solar, I designed AWS-based monitoring and analytics for solar farm operations and IoT telemetry, handling 100M+ records with near real-time processing using AWS Lambda, S3, and EC2. I improved processing efficiency by 20% through optimized transformation workflows and Parquet storage, reducing compute/storage costs by an estimated $50K annually, and built a dedicated testing data pipeline that cut data validation and testing overhead by 60%. Earlier, at Exposys Data Labs, I built an MLOps pipeline for customer purchasing behavior (ZenML + Scikit-learn) and delivered a FastAPI prediction service for internal dashboards.