Fernando Zepeda

Software Engineer.

Experience

Working on analytics projects for commercial vehicles as a Data Engineer.
Mainly focusing on backend development for data applications and deploying services across various cloud platforms like Azure and AWS.

As an ML & Data Engineer, I worked on multiple projects in the Energy sector.

Some key projects:

Designed and implemented cloud infrastructure and serverless data pipelines for various use cases within the generation division. Tech stack: Azure, Azure Functions, Event Grid, Python, Azure Cosmos DB, .NET, Infrastructure as Code, Microservices Architecture.
Led data migration efforts and implemented an event-driven architecture using Kafka for a major manufacturing client. Tech stack: Kafka (Confluent), Python, GitHub Actions, Docker, Grafana, Prometheus.

I served as a consultant on various Data Engineering, Analytics, and Cloud Infrastructure projects.

Noteworthy projects:

Developed a Big Data pipeline for an internet company. Tech stack: Exasol, Kubernetes, Python.
Built an application for price adjustments for a leading German utility company. Tech stack: Azure Cloud Stack, Snowflake, Python.
Optimized data pipelines for a major German TV data provider. Tech stack: Snowflake, Tableau Server, Python, Amazon S3.

I worked as a Data Scientist and Data Engineer on various projects in Flight & Ground Ops, Revenue Management, and Pricing.

Selected projects:

Integrated a solver for all operational modules on Lufthansa Group’s new operations platform with Google Cloud to support the Integrated Operations Control Center in Zurich.
Ops Predictive Analytics: Predicted stranded passengers to forecast hotel demand in Frankfurt and Munich hubs.
Rapid prototyping and modeling for dynamic bundling and pricing of flight ancillaries using machine learning classifiers.

Focused on Data Science and Rapid Prototyping in Revenue Management & Pricing.

Developed an optimal price calculation model for single flights using Reinforcement Learning.
Estimated passenger demand and dynamic pricing for direct flights using machine learning algorithms.

M.Sc. in Industrial Engineering - Technical University of Berlin
Master’s Thesis: "An Airline Stochastic Dynamic Pricing Model"
Ing. Civil in Industrial Engineering - Universidad Técnica Federico Santa María
Diploma Thesis: "Predicting Bankruptcy of Chilean Enterprises with Artificial Neural Networks"

Airline Group of the International Federation of Operational Research Societies (AGIFORS 2019) - Panama 2019
Presented at the Revenue Management SG yearly meeting ⬀.
International Conference on Operations Research (OR 2017) - Berlin 2017
Speaker Presentation ⬀ | Program ⬀.

Some highlights:

Skill	Technologies
Cloud & Infrastructure as Code	GCP, Azure, AWS, Terraform
Cloud Deployments: Container as a Service	Docker, Kubernetes, Azure Container Registry, Docker Compose
Cloud Deployments: Functions as a Service	Azure Functions (Durable), AWS Lambda, OpenFaaS
Databases	SQL Server, MySQL, PostgreSQL, Oracle DB, MongoDB, AWS RDS
Data Warehouses	Snowflake, Google BigQuery, Azure Synapse, Exasol
DevOps	Azure DevOps, GitLab CI, Gerrit, Jenkins, GitHub Actions
Data Engineering	Apache Kafka, -Hive, -Spark, -Airflow, Google Dataflow, Azure Databricks, -Data Factory
Frontend Programming	ReactJS, TypeScript, Gatsby JS
Machine Learning	PyTorch, TensorFlow, Keras, scikit-learn, R-caret
Programming	Python, JavaScript, R, Bash, PowerShell, Rust
Other Tools	Jira, Confluence, Agile Development


Genetic Algorithm: Implemented a genetic algorithm optimizer using base Python NumPy
Notes on Azure Data Engineering: Compiled notes for the Azure DP203 Exam