🚀 Lead Data Engineer | Cloud & ETL Specialist | FinTech & Startups
I lead data teams in FinTech, focusing on modern data infrastructure, scalable ETL pipelines, and cloud-native solutions. Over the past decade, I've built several data platforms and teams, spanning full end-to-end solutions with engineers, data scientists, analysts, and ML engineers.
- Building scalable ETL pipelines using Dagster, dbt, and Kubernetes.
- Designing cloud-native data solutions with Azure & AWS.
- Developing API connectors with dlt, DuckDB, and Polars.
- Modernizing legacy Java data pipelines to Python-based architectures.
Programming & Query Languages:
Cloud Platforms:
Data & Transformation Tools:
Development & Infrastructure:
Building a new architecture migrating from multi-cloud to single-cloud solutions with focus on:
- Modular data architecture with clear separation of bronze/silver/gold/semantic layers
- LLM-ready data platforms optimized for AI/ML workloads
- Cloud-native ETL using modern data stack principles
Built a scalable Kubernetes architecture leveraging dlt, dbt, and DuckDB to efficiently load data from Azure Blob Storage to Azure SQL Server, creating a robust and maintainable data pipeline.
- agile-ai ⭐ – AI-powered agile project management tools
- Modular API Ingestion – Built an extendable API ingestion framework using dlt, DuckDB, and Python, improving data integration efficiency
- Automated Data Processing Pipelines – Created dbt-driven transformation workflows, enabling structured reporting and analytics
- Dagster & dbt on Kubernetes – Designed a fully automated cloud-native data pipeline, reducing manual intervention and improving observability
- 🏗 GitHub
- 📝 Personal Blog (archives of data engineering insights)