Data Engineering
Your data, actually working for you.
We build automated data pipelines that collect, clean, transform, and route your data — so your systems stay in sync, your decisions stay accurate, and your team stops doing manually what should be automatic.
3+ hrs
Saved weekly on reporting
6
Avg. sources unified
< 4h
Data freshness
// what_it_is
What is Data Pipelines & ETL?
A data pipeline is the automated process that moves data from where it's generated (your CRM, your e-commerce platform, your ads accounts) to where it's useful — cleaned, transformed, and ready to query. Without one, that work happens manually in spreadsheets.
// the_problem
Does this sound familiar?
Data scattered across 5+ tools. Shopify, Meta Ads, your CRM, your inventory system — none of them talk to each other and none of them agree on the numbers.
Hours lost to manual reporting. Someone on your team is pulling exports, pasting into sheets, and reconciling data every week. That's expensive and error-prone.
Decisions made on stale data. By the time you see what happened, it's too late to act. You need data that's hours old, not weeks old.
No single source of truth. Different people in your business are looking at different numbers and drawing different conclusions. That's a pipeline problem.
// what_we_build
What we deliver
01
Multi-source ETL pipelines
Extract from APIs, CSVs, databases, and webhooks — clean, normalize, and load into your warehouse on a schedule.
02
dbt transformation models
Business logic defined as SQL models — CAC, LTV, margin, cohorts — version-controlled and testable.
03
Scheduled automation with Airflow
Pipelines that run on a schedule, retry on failure, and alert you when something goes wrong.
04
Real-time sync pipelines
Event-driven pipelines that update your warehouse minutes after something changes in a source system.
05
Data quality monitoring
Automated checks that flag anomalies — missing data, unexpected nulls, values out of range — before they reach your dashboard.
// how_it_works
How we work
1
Audit your data landscape
We map every data source, understand the shape of each, and identify the joins and transformations needed.
2
Design the pipeline architecture
Extraction strategy, transformation logic, load targets, scheduling, and failure handling — all defined before we build.
3
Build incrementally
We ship one source at a time, validating each before adding the next — so you see value early and problems surface quickly.
4
Deploy & document
Running on your infrastructure with full documentation of every model, schedule, and data contract.
// tech_stack
Technology we use
Extraction
Python + APIs
Transformation
dbt
Scheduling
Apache Airflow
Warehouse
Supabase / PostgreSQL
Monitoring
Alerting + logs
Hosting
Vercel / AWS
// related_work