// Services / Data analytics

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.

retention.sql
-- 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;

// Capabilities

What we deliver.

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.

// Stack

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.

BigQuery PostgreSQL Python Cloud Build Google Cloud Platform AWS ETL Cloud Run SQL

// Answers

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, but we design against the cloud you already run — Redshift, Athena, and the wider AWS data stack included — and deploy into your accounts.

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.

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