Data Platform Architecture
Data platform architecture is how we turn raw data into a strategic asset for US enterprises. From ingestion to analytics, our data engineering creates reliable, governed, and scalable data infrastructure.
We assess your data maturity and build incrementally, delivering value at every stage. Our data platforms include ingestion, transformation, storage, and consumption layers designed for your specific needs.
Capabilities
Modern Data Stack
Snowflake, BigQuery, or Databricks architectures built for scale.
Ingestion Pipelines
Reliable data pipelines from Fivetran, Airbyte, or custom sources.
Data Lake Architecture
Lakehouse architectures for raw and processed data storage.
Reverse ETL
Census, Hightouch, or custom reverse ETL for operational activation.
How It Comes Together
A typical data platform architecture
Source systems and events land reliably in a raw layer.
dbt models turn raw data into trusted, tested metrics.
Governed storage optimized for your query patterns.
Dashboards and reports surface insight for decision-makers.
Reverse ETL syncs insight back into the tools your team uses.
In Practice
A data platform's real test isn't the initial build, it's whether it still makes sense eighteen months later when data volume has tripled and three new source systems have been added. We design ingestion, transformation, and storage layers as independently evolvable components rather than a single tightly coupled pipeline, so adding a new source or changing a downstream consumer doesn't require touching everything else. Schema evolution is planned for explicitly: source systems change their data shapes without warning, and a platform that breaks every time that happens creates more operational burden than the analytics it was built to enable. We default to a modern lakehouse pattern, raw data landed close to its source format, transformed through clearly staged layers, with data contracts between teams that produce data and teams that consume it, so ownership and quality expectations are explicit rather than assumed. Every platform we build includes cost monitoring from day one, since storage and compute costs on modern data platforms scale with usage in ways that are easy to lose track of until the bill arrives.
Related Services
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