Client Case Studies
Real companies. Real problems. Here's how we solved them.
Who We Work With
Most of our clients come to us at the same point: the company is growing, data lives in too many places, and the person who's been holding it all together can't keep up anymore. They don't need a $300K full-time hire — they need someone who's done this before and can get it right the first time.
We work with companies between 10 and 200 people who are at exactly that moment.
Building a dbt Data Foundation from Scratch: A Case Study for Startups Without a Data Team
Health tech · ~50 employees · No data person, just a CEO, AI, and a lot of manual checks. Now 12 people rely on the same infrastructure.
0 → 1
dbt data foundation built from scratch
10 hrs/week
given back to the CEO by eliminating manual data validation
Automated
data quality checks running continuously
Tech: dbt · dbt Cloud (free tier) · PostgreSQL · Data Modelling · dbt Tests · Calculated Columns · dbt Documentation
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Building a Self-Serve Analytics Culture with Metabase: A Case Study for Startups Without a Data Team
Health tech · ~50 employees · 11 non-technical users now making decisions from data, without asking anyone for help
0 → 1
Metabase built and adopted from scratch
11
non-technical users actively using self-serve analytics
4 departments
onboarded: marketing, sales, operations, C-level
Tech: Metabase · Metabase (basic plan) · dbt · PostgreSQL · Workshop Design · Notion · Data Governance · Self-Serve Analytics
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Building an AWS Event Tracking Pipeline from Scratch: A SaaS Case Study
Flexible office marketplace SaaS · ~150 employees · Fast-growing, investment-backed · Full Pentaho to AWS migration
2 weeks
to get the pipeline live
5,000+ events/day
processed through the pipeline
Pentaho to AWS
legacy infrastructure modernised
Tech: AWS Lambda · AWS S3 · AWS Glue · PostgreSQL · Segment.io · Data Modelling · Data Partitioning · Cloud Infrastructure
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Replacing a Legacy ETL Tool with AWS: A SaaS Migration Case Study
Flexible office marketplace SaaS · ~150 employees · Fast-growing, investment-backed · Full Pentaho to AWS migration
6 months
to fully replace Pentaho with a modern AWS stack
4 sources integrated
PostgreSQL, Pipefy, HubSpot, Segment.io
30+ pipelines
running in Airflow
Tech: Apache Airflow · AWS Lambda · AWS S3 · PostgreSQL · Pipefy · HubSpot · Segment.io · Python · SQL · Parquet · Data Modelling
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Building a Data Lakehouse from Scratch on AWS: A Case Study for Complex Organizations
Multinational organization · ~500 employees · $32M revenue · No prior data infrastructure
40% under budget
data platform delivered
130+ pipelines
running in Airflow across all sources
72 hours
from development to production for new pipelines
Tech: Apache Spark · Apache Airflow · Apache Iceberg · AWS · Terraform · Docker · PostgreSQL · Salesforce · Freshdesk · Google Analytics · Metabase · Python · SQL
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Working with a similar challenge?
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