Data Engineering
We build the pipelines, warehouses, and analytics infrastructure that turn raw data into reliable business intelligence, fast, clean, and maintainable.
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Avg. query speed improvement
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Time freed from manual data prep
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Pipeline uptime SLA
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Data systems integrated
What it is
Most companies have data sitting in five different systems with no reliable way to combine it. Dashboards break when someone renames a column. Analysts spend 60% of their time cleaning data instead of analyzing it. We build the infrastructure that fixes this: ingestion pipelines, transformation layers, a warehouse your whole team can query, and monitoring that tells you when something breaks before your CEO notices.
Why it works
One source of truth across all your systems, CRM, product analytics, payments, support, structured so any analyst can query it without a data engineer on call.
Automated pipelines that move data from source systems to your warehouse on schedule, with error handling, alerting, and automatic recovery.
Your raw data cleaned, joined, and modeled using dbt so your business metrics are defined once, tested, and consistent across all dashboards.
Automated tests that catch broken pipelines, schema changes, and anomalous values before they corrupt reports or trigger wrong decisions.
Pre-aggregated, denormalized tables built for fast BI tool queries. Connect Metabase, Looker, Tableau, or Power BI without waiting 30 seconds per query.
Systems designed to handle 10x your current data volume without rewriting. We build with growth in mind, not just for today's load.
The process
Step 01
We map your current data sources, identify quality issues, and understand how your team actually uses data today. This shapes the architecture.
Step 02
We propose a warehouse and pipeline architecture based on your data volume, team size, and BI tool requirements. You review before we build.
Step 03
We build connectors for each data source, set up your warehouse (BigQuery, Snowflake, or Redshift), and get all data flowing reliably.
Step 04
We write dbt models that define your business metrics: revenue, activation, churn, LTV. Tests and documentation included.
Step 05
Automated pipeline monitoring, alerting, and documentation. We train your team and stay available for 30 days post-launch.
Best fit
Your data is in five tools and nobody agrees on the numbers. Time to build a real warehouse before the inconsistencies cause real problems.
Your analysts waste hours every week fixing broken reports. We rebuild the foundation so they can focus on actual analysis.
You acquired a company and now have two systems of record for customers. We merge and unify the data infrastructure.
Product teams that want to build data-driven features, recommendations, personalization, risk scoring, and need reliable data infrastructure underneath.
Our stack
FAQ
Fix your data infrastructure
A 1-week data audit gives you a clear picture of what's broken and a prioritized plan to fix it. Most clients start building within 2 weeks.
