From raw events to board-ready dashboards.
We build the data infrastructure that lets you make decisions with confidence — pipelines, warehouses, and analytical surfaces engineered to scale.
-- 7-day retention by signup cohort
with cohorts as (
select user_id,
date_trunc('week', created_at) as cohort_week
from users
),
activity as (
select user_id,
date_trunc('week', event_at) as active_week
from events
where event_name = 'session_start'
group by 1, 2
)
select c.cohort_week,
count(distinct c.user_id) as signups,
count(distinct a.user_id) as retained,
round(count(distinct a.user_id)
/ count(distinct c.user_id)::numeric, 3) as retention
from cohorts c
left join activity a
on a.user_id = c.user_id
and a.active_week = c.cohort_week + interval '7 days'
group by c.cohort_week
order by c.cohort_week;What we deliver.
The instrumentation, the warehouse, and the analytical surface are one system. We engineer them together, because a dashboard is only as trustworthy as the pipeline feeding it.
Data pipeline architecture
Reliable, scalable pipelines moving data from source systems into analytical layers without loss or latency.
ETL and ELT engineering
Extract, transform, and load workflows engineered for throughput and correctness — with monitoring, alerting, and replay built in.
Analytics dashboards
Business-facing dashboards that surface the right metrics to the right stakeholders. Built to be maintained, not abandoned.
Data warehouse design
Schema design, partitioning strategy, and query optimization on BigQuery and PostgreSQL for fast analytical access at scale.
Event tracking and instrumentation
Structured event schemas, tracking plans, and server-side instrumentation so product data is trustworthy from day one.
Source to decision.
Every stage is monitored, alerted, and replayable. When a number looks wrong, the answer is a lineage question, not an argument.
Technology we work in.
BigQuery-first where it fits, with the full AWS data stack on the table — we build against the cloud you already run.
- BigQueryPostgreSQLMySQL
- PythonSQLETLCloud Build
- Power BI
- Google Cloud PlatformAWSCloud RunCloud StorageS3Lambda
Data and analytics, answered.
Which cloud do you build data infrastructure on?
Both Google Cloud Platform and AWS. We default to BigQuery for warehousing where it fits, and otherwise build against the cloud you already run — deploying into your own accounts on services like Cloud Run, Cloud Storage, S3, and Lambda. Nothing is locked to a proprietary stack.
Can you work with the data we already have, however messy?
Yes. Most engagements start with sources that grew organically — inconsistent schemas, gaps, and duplication. We profile what exists, document the issues, and build transformation and validation into the pipeline rather than assuming clean inputs.
Do you build the dashboards, or just the pipelines underneath?
Both, and we treat them as one system. A dashboard is only as trustworthy as the pipeline feeding it, so we engineer the instrumentation, the warehouse, and the analytical surface together — and hand over something your team can maintain.
How does data work fit the 7-day sprint?
Data engineering increments cleanly. A sprint might land one reliable pipeline, a validated warehouse schema, or a dashboard backed by trustworthy metrics — a working, reviewable piece of the system every week rather than a quarter-long black box.
Which databases and BI tools do you work with?
For storage and warehousing: BigQuery, PostgreSQL, and MySQL, with Firebase where a real-time or app-backed store fits. For analysis we work in Python and SQL with ETL/ELT pipelines, and we build business-facing dashboards in Power BI or as custom analytical surfaces — whichever your team will actually maintain.