End-to-end data engineering. From raw source chaos to warehouse-ready, insight-primed pipelines. Kafka, Spark, dbt, Airflow. We speak the language of your data infrastructure.
Every data problem is an architecture problem. We solve both.
Kafka-powered event streaming, CDC pipelines, webhook collectors, and batch ingestion at any scale. Sub-second latency from source to sink.
dbt-powered modular SQL transformations, Spark jobs for heavy ETL, and streaming transforms with Flink. Clean data contracts every step.
Snowflake, BigQuery, and Redshift architecture, optimization, and cost engineering. We turn runaway compute bills into efficient, predictable spend.
Airflow DAGs that actually work. Prefect flows, Dagster assets. We build orchestration that handles failures gracefully and alerts that make sense.
Great Expectations checks, Monte Carlo integration, custom anomaly detection. Know when your data breaks before your stakeholders do.
Feature stores, training data pipelines, model serving infra. Bridge the gap between data engineering and machine learning.
We work with the tools your team already uses, and introduce the ones they should.
We map your current data landscape: sources, flows, pain points, scale requirements. Two weeks, full technical audit.
A battle-tested architecture proposal with tech stack, cost projections, and migration path. No surprises.
Parallel build with zero-downtime migration. We run old and new simultaneously until you're confident.
Ongoing support, 24/7 alerting, quarterly reviews. Your team learns, your infrastructure grows.
Book a free 20-minute data audit and we'll walk through your current setup, identify what's costing you time or money, and outline what a clean, scalable stack would look like for your team. No sales pitch. Just a technical conversation.
Engagements are scoped based on your stack and goals. We'll figure out what makes sense on the call.