🚀 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 scalable, modular data architectures, with a recent focus on:

• GitHub Data Processing pipelines utilizing Databricks Unity Catalog.
• High-performance API ingestion combining uv, dlt, DuckDB, and dbt.
• LLM-ready data platforms optimized for AI/ML workloads.

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.

• github-trend-insights 📊 – Data pipeline analyzing GitHub trends using dlt, DuckDB, and dbt.
• agile-ai ⭐ – AI-powered agile project management tools.
• Scalable Data Ingestion App 🚀 – Built an extendable API ingestion framework using uv, dlt, DuckDB, and pydantic, 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.

• 💼 LinkedIn
• 📧 Email
• 🏗 GitHub
• 📝 Personal Blog (archives of data engineering insights)