Resume

As a Software and Data Engineer at Airbus, I was responsible for developing data pipelines, analyze large data sets to identify key insights for predictive maintenance tasks and ensure pipeline robustness. I worked collaboratively with cross-functional teams to develop data-driven solutions and improve data quality and accuracy.

 

I managed aircraft timeseries data using Java, Spark, Javascript and Python. I devised a robust end-to-end ETL framework for big-data to efficiently collect data from a diverse fleet of over 60 aircraft, centralizing it into a data lake. These data pipelines were successfully deployed, providing decoded aircraft data through the Skywise platform. I engineered and fortified pipelines to ensure they could smoothly handle ingestion irregularities. I optimized the partition logic for output Parquet files, drastically reducing computation costs by 80% and storage costs by 95%. Furthermore, I applied my skills in Pyspark, SQL, and JavaScript to design an envelope-based statistical qualification algorithm for anomaly detection in timeseries data. This system was seamlessly integrated with an interactive dashboard, enabling real-time result publication and in-depth analysis of data losses.