Ashutosh Saini

Designing and implementing an end-to-end Data Freshness Monitoring Framework for the production data warehouse Gold Layer to guarantee SLA compliance for downstream analytics and business intelligence. Establishing an SLA schema that maps out maximum acceptable data lag thresholds across enterprise-critical core tables. Use Apache Airflow to evaluate and log real-time table update frequencies against established SLAs. Developing an automated data quality notification loop, consolidating pipeline latency

At an Intensive 10 Months Bootcamp @WBS Coding School Core Engineering & Scripting: Developing robust data handling workflows using Python (Pandas, Functional Programming, API Extraction) and Advanced SQL for complex data manipulation and transformation. Data Warehousing & Cloud Architecture: Architecting cloud-native data solutions using Google BigQuery and the Microsoft Azure Ecosystem (Azure Data Factory, Azure SQL, Relational/NoSQL DBs). Production Pipeline Design: Implementing end-to-end ETL/ELT pro

Conducted an architectural study on AI-driven credit scoring systems for SMEs, focusing on integrating alternative data sources (transactional and behavioral signals). Evaluated the data infrastructure required to support autonomous risk monitoring. Identified that the core bottleneck for scalable financial AI is not the model, but the integrity and reliability of the underlying ETL/ELT pipelines. This realization drove my decision to transition into intensive, full-time Data Engineering training at WBS C

Monitored false price indications in stocks, commodities, and futures in real-time using the Grafana dashboards. Created more than 10 test accounts using Open API Swagger [GET, POST, and PUT] methods to make data available to customers. Solved more than 100 service requests and technical queries related to the historical data, mostly pricing misinformation and data server lag. Calculated the monthly basis-point discrepancies in the stock price data using Colab(Python script).

Developed the backend code of a 3-tier application in Python and displayed the annual key figures of the structured products(discount certificates) on the UI. Constructed the automated data pipeline using Postgres DB(SQL scripts). Python(Pandas, Numpy, and SQLAlchemy ) and REST API. Created the Docker image to containerize the backend code and dependencies before deploying to the production environment.

Developed the backend code of a 3-tier application in Python and displayed the annual key figures of the structured products(discount certificates) on the UI. Constructed the automated data pipeline using Postgres DB(SQL scripts). Python(Pandas, Numpy, and SQLAlchemy ) and REST API. Created the Docker image to containerize the backend code and dependencies before deploying to the production environment.

Managed high-volume picking and staging workflows in a fast-paced logistics environment, ensuring accurate inventory allocation for overflow volumes. Utilized handheld scanner hardware and integrated warehouse management systems to track and reconcile package barcodes, maintaining 100% location accuracy during sortation. Proactively audited and rectified container locations using digital tracking tools to minimize staging errors and optimize facility throughput.

Sensor Commissioning: Successfully commissioned the Specim FX17 hyperspectral camera, integrating high-resolution spectral hardware into the research workflow. Spectral Data Acquisition: Executed end-to-end data capture protocols to isolate and analyze spectral signatures of diseased plant leaves, creating high-fidelity training datasets. Image Processing & Analysis: Developed Python-based image analysis pipelines using the Harvester library to extract meaningful spectral features from raw hyperspectral i

Sorting and Scanning Associate at Amazon Logistics Warehouse in Borsigturm, Berlin, Germany