Data, Analytics & BI
From data sprawl to decision velocity.
Modern data platforms, warehouses, lakehouses, dashboards and ML pipelines — wired to the metrics your CFO actually tracks.
What we do
Outcomes, not output.
- Data strategy and maturity assessment
- Modern data warehouse (Snowflake, BigQuery, Redshift)
- Lakehouse architecture (Databricks, Delta Lake)
- ELT / ETL pipelines with dbt, Fivetran, Airbyte
- Reverse ETL and operational analytics
- BI dashboards (Looker, Power BI, Tableau, Metabase)
- Real-time streaming analytics (Kafka, Flink)
- Data governance, catalog and quality monitoring
Outcomes
What good looks like.
<0 min
Average data freshness from source to warehouse
0×
Faster ad-hoc query performance after migration
0 days
From warehouse-live to first production dashboard
How we engage
A clear path from kickoff to handoff.
01
Audit
Map your current data landscape, sources and consumers.
02
Design
Data model, warehouse architecture and governance framework.
03
Build
ELT pipelines, warehouse and semantic layer.
04
Visualize
BI dashboards tuned to decision-maker workflows.
05
Govern
Data quality monitoring, lineage and ongoing optimization.
Stack
Tools we use day-to-day.
SnowflakeDatabricksBigQueryRedshiftdbtFivetranAirbyteLookerPower BITableauKafkaSparkAirflowMetabase
Who it's for
Built for these teams.
Mid-market with data sprawl
Multiple BI tools, conflicting numbers, no shared metrics layer.
Series C+ scaling analytics
Outgrew startup-era stack — need a real warehouse and governance.
Operators who want self-serve
GTM and finance teams who need clean metrics without engineering bottlenecks.
FAQ
Common questions, answered.
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