Data engineering: building a modern data stack

A modern data stack typically includes ingestion (Fivetran, Airbyte, or custom), a warehouse (Snowflake, BigQuery, Redshift), transformation (dbt), and orchestration (Airflow, Dagster, or cloud-native). The goal is reliable, repeatable pipelines that turn raw data into analytics-ready tables.

We help teams choose the right level of tooling—start simple, add orchestration and transformation as complexity grows. We also emphasize data quality, documentation, and ownership so that the stack stays maintainable.

As you scale, lineage and impact analysis become important: when a source or model changes, you need to know what breaks. We integrate checks and documentation into the workflow so that the data team can move fast without creating hidden dependencies.

If you're scaling data engineering or modernizing legacy ETL, we can help design the architecture and implement the first pipelines.