// 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.

Warehouse
BigQuery PostgreSQL MySQL
Processing
Python SQL ETL Cloud Build
BI & dashboards
Power BI
Cloud
Google Cloud Platform AWS Cloud Run Cloud Storage S3 Lambda

// 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, 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.

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