Nicolai Negru

Modernized and expanded an application's functionality as a senior Python engineer, quantifying the risk of trades that, according to legislation, must be cleared via clearinghouses.

I enabled the automatic assessment of trades in several stages, starting with fast conservative estimates and progressively including more detailed information when more precise margins have to be calculated. I used technologies such as Python, Oracle Database and SQL, IBM MQ, event-driven architecture, XML, and FpML.

A web application for project progress tracking that I designed and implemented as a Python engineer.

The web application enabled delivery leads to report on the progress of their projects—providing senior management with a clear view of which projects are on track and within budget. I also integrated 3rd-party APIs and systems for authentication and data exchange.

Additionally, I designed and implemented a PoC synthetic data generator application that accepted data schema definitions, including shape, format, and volume, and generated large volumes of data per the requirements. I used Oracle Database, JavaScript, HTML, OAuth 2, and Python technologies such as FastAPI, SQLAlchemy, and pandas.

Web APIs that processed event streams from customers navigating a large retailer's website.

As a senior Python engineer, I maintained and expanded the web APIs, pushing incoming data streams through Databricks pipelines and generating shopping recommendations to be displayed to customers based on the analysis of historical shopping patterns. I used PySpark, pandas, Azure Databricks and Data Factory, Kafka, MongoDB, and Kubernetes.

A thorough review of existing data pipelines, which the company could not maintain and evolve because of their complexity.

After the inventory, I also redesigned the data platform as the Python data engineer on the project, matching the company's business needs and technical capabilities. The new architecture demonstrated to the business and technical groups how it could be scaled to accommodate new data feeds. Finally, I implemented the streamlined platform that consumed several streaming and batched data feeds, which the technical teams could further expand. I used PySpark, pandas, Delta Lake, and Azure: Databricks, Data Factory, and Key Vault.

Web APIs and data pipelines that consumed trading data to identify traders' behavioral patterns.

I maintained and expanded the web APIs and data pipelines, analyzing traders' performance in light of historical market conditions to issue behavioral recommendations for increased traders' efficiency. I used pandas and Amazon: EC2, RDS for PostgreSQL, S3, Lambda, and AppSync.