Scattered data in, decisions out

What we actually do

Data work fails quietly: silent pipeline breaks, metrics nobody trusts, dashboards nobody opens. We engineer against all three with contracts, tests, and observability across the whole path from source to decision.

• Pipeline engineering - batch and streaming ingestion with schema contracts, idempotent loads, and alerting that fires before your CFO notices
• Warehouse and lakehouse builds - dimensional models and semantic layers on Snowflake, BigQuery, Databricks, or Postgres when that's honestly enough
• Analytics engineering - version-controlled, tested transformations so every metric has one definition and one owner
• Data quality frameworks - freshness, volume, and distribution checks wired into CI with clear ownership when they fail
• Decision surfaces - dashboards and operational reports designed around the decision they serve, not the chart library

Where this earns its keep

Mid-sized companies that have outgrown spreadsheet-and-hero-analyst but can't justify a 20-person data platform team. We build the enterprise-grade version at mid-market weight - and it stays maintainable after handover.

How an engagement runs

We start with the decisions you can't make today and work backwards to the data. One-page brief, fixed-price plan, senior-only build, clean handover with documentation your own engineers can extend.