From raw data to clear decisions

Data is only useful if people can get at it and trust it. I build the pipelines, dashboards, and analysis tools that make that happen.

What I build

• ETL pipelines: Extract, transform, and load data from multiple sources into a unified data warehouse
• Custom dashboards: Real-time and historical dashboards built around your own KPIs
• Automated reporting: Scheduled reports delivered via email, Slack, or embedded in your application
• Data warehousing: Centralized, query-optimized data stores built on PostgreSQL, BigQuery, or Snowflake
• Anomaly detection and forecasting: Anomaly detection, trend forecasting, and natural language querying of your data

My approach

I start by understanding what decisions your data should inform. Then I map your data sources, design the schema, build the pipeline, and deliver dashboards that anyone on your team can read.

Tech I use

PostgreSQL, BigQuery, and Snowflake for storage. Python and SQL for transformation. Custom-built frontends with D3.js or Chart.js for visualization. Apache Airflow or custom schedulers for orchestration.

Use cases

• Executive dashboards that aggregate sales, marketing, and operational metrics
• Customer analytics platforms that track behavior, retention, and lifetime value
• Supply chain visibility tools that monitor inventory, shipments, and supplier performance
• Financial reporting systems that automate month-end close processes

Related reading

• Less Is More: Why I Reach for Simpler ML Models First: the same discipline applies to analytics stacks