Data Engineering
We build the data infrastructure that powers analytics, ML, and reporting — from ingestion pipelines to semantic layers — so your data team has clean, governed, fast data.
What we deliver
Six areas of focused delivery within the Data Engineering practice.
Data Pipeline Architecture
Batch and streaming pipelines using Airbyte, Fivetran, dbt, and Apache Kafka designed for reliability and observability.
Data Warehouse Design
Snowflake, BigQuery, and Redshift schemas with medallion architecture, incremental models, and semantic layer definitions.
Real-time Analytics
ClickHouse, Apache Flink, and Materialize for sub-second analytics on high-velocity event streams.
ML Feature Engineering
Feature stores, training data pipelines, and experiment tracking infrastructure to accelerate ML development cycles.
Data Governance & Quality
dbt tests, Great Expectations, and Soda Core checks with data contracts enforced at pipeline boundaries.
BI & Visualization
Metabase, Looker, and custom React dashboards on top of well-modeled data layers for self-serve analytics.
Where teams apply Data Engineering
These are the problems we most commonly solve. Every engagement starts with your specific context, not a template.
Central data warehouse from 10+ operational sources
Real-time event streaming for product analytics and personalization
ML training data pipelines for recommendation and fraud systems
Self-serve analytics infrastructure for business intelligence teams
Technologies and tools
Selected to fit the problem, not the trend.
Let's talk about Data Engineering
Tell us what you are building and where you are stuck. We will respond with honest scoping.