Modern data stack
Snowflake, Databricks, BigQuery. dbt for transformations, Fivetran/Airbyte for ingestion, Airflow/Dagster for orchestration.
Solutions / Data & BI
Data work that your CFO will actually trust.
Most analytics projects produce dashboards nobody opens. Ours produce a data foundation your CFO trusts and your operators use — because the numbers tie out, the lineage is clear, and the semantics are governed.
4×
Faster query performance (median)
9 wks
Median time to first executive dashboard
99.7%
Data freshness within SLA (post-foundation)
0
Reconciliation surprises in finance
What it is
The work, plainly described.
Data Analytics & BI is our solution for organizations that have data but not insight. We build the modern data stack — Snowflake, Databricks, or BigQuery foundations with dbt models, a semantic layer, and the BI tools your operators actually use. The result is one set of numbers everyone trusts, with the lineage to defend any value to anyone who asks.
Where it fits
- Data foundation buildersYou've outgrown the executive Excel dashboard. Time for a real data platform.
- BI consolidationYou have Tableau, Power BI, Looker, and three flavors of "my CSV" — and the numbers don't agree.
- Analytics maturity upliftYou have a data warehouse but it's a swamp. We help you bring the discipline.
- AI/ML enablementYou want to build ML models but the data foundation isn't ready. We build the foundation first.
Capabilities
What we'll actually do.
Each of these is a deliverable category, not a buzzword bullet. We scope, build, and stay accountable for each one.
Semantic layer
dbt Semantic Layer, Cube, Looker LookML. Governed metrics, consistent definitions, defensible numbers.
Source ingestion
ERP, CRM, marketing, support, product analytics, financial systems — every line-of-business source connected and governed.
Data modeling
Dimensional and event-based models, slowly-changing dimensions, late-arriving facts. The boring data engineering that determines whether analytics works.
Self-service BI
Tableau, Power BI, Looker, Sigma, Hex — chosen for the operators who'll use them.
Data governance
Lineage, access controls, PII tagging, GDPR/CCPA-aware retention, and the data catalog your data team should have.
Process
How an engagement actually runs.
No mystery, no shifting goalposts. Five phases with measurable outcomes per phase.
Data audit
Sources, current state, decision-making maturity, and the analytics gaps. Output: a maturity assessment with prioritized roadmap.
Foundation build
Cloud data warehouse, ingestion, transformations, and semantic layer foundation. Two-month foundation phase.
Dashboard delivery
First executive dashboard in week 9. Operational dashboards in subsequent waves.
Self-service enablement
Training your analysts on the semantic layer and the BI tool. The handoff that makes analytics sustainable.
Sustained delivery
Optional retainer for new dashboards, model improvements, and the discipline of monthly metric reviews.
Why us
Three things you should know.
Numbers tie out
Reconciliation discipline is non-negotiable. Every dashboard ties to a defensible source. Period.
Semantic layer is the spine
We build governed metrics first, dashboards second. Most BI failures are governance failures, not visualization failures.
Engineers, not just analysts
Our data team includes data engineers, not just analytics consultants. We build the platform — and it stays built.
Frequently asked
The questions everyone asks.
Do you have a preferred data warehouse?
We're experienced on Snowflake, Databricks, and BigQuery. We pick based on your existing investments, team skills, and cost profile.
Do you do real-time analytics?
Yes — Kafka, Kinesis, and event-driven architectures for use cases that need real-time. We don't over-engineer batch problems with streaming.
What about ML on top of analytics?
Yes — see AI & Data Solutions. The data foundation we build is purpose-built to enable ML downstream.
Can you migrate from a legacy DW (SQL Server, Oracle)?
Yes. We've done several migrations including the messy parts — stored procedures, ETL packages, and reports that nobody knew were in use.
How do you handle data quality?
Tests in dbt, freshness SLAs, anomaly detection, and the governance rituals (data review meetings, owner-tagged metrics) that make data quality everyone's job.