Turn Raw Data Into Real Insight
Messy data? Incomplete pipelines? I help teams build streamlined, automated data systems that are fast, reliable, and easy to scale. The goal is simple: deliver clean, usable data to the right people at the right time.
Fixing the Pain Points
Manual, Error-Prone Processes
Outdated or DIY workflows waste time and cause issues. I bring in automation with tools like Airflow, dbt, and custom scripts to eliminate bottlenecks.
Poor Data Governance
I implement robust frameworks for data quality, lineage, and version control—so you always know where your data came from, and that you can trust it.
Slow, Inaccurate Reporting
I speed up reporting pipelines and ensure data consistency across tools, using platforms like Databricks, Snowflake, or BigQuery.
My Engineering Principles
Automated, Reproducible Pipelines
I use modern tools like dbt, Airflow, and GitOps practices to make your data workflows reliable, testable, and versioned.
Trustworthy Data Quality
From validation to alerting, I bake quality checks directly into your pipelines to catch problems before they become fire drills.
Performance and Flexibility
I balance simplicity with speed—building pipelines that handle scale without becoming overly complex or locked in.
My Approach
1. Discovery & Workflow Review
I take a close look at your current setup, data sources, and blockers to map out what’s working and what’s not.
2. Pipeline Design & Tooling Selection
Together, we choose the best technologies for your stack and goals—whether that’s managed services or open source tools.
3. Build, Test, Launch
I implement, document, and deliver your new workflows—ready to run and easy to evolve as your business grows.